nils-herrmann/clean_scirepeval huggingface dataset:
RNN-based Learning of Compact Maps for Efficient Robot Localization

We describe a new algorithm for robot localization, efficient both in terms of memory and processing time. It transforms a stream of laser range sensor data into a probabilistic calculation of the robot’s position, using a bidirectional Long Short-Term Memory (LSTM) recurrent neural network (RNN) to learn the structure of the environment and to answer queries such as: in which room is the robot? To achieve this, the RNN builds an implicit map of the environment.
Low-cost antennas for mm-Wave sensing applications using inkjet printing of silver nano-particles on liquid crystal polymers

For the first time, we demonstrate the possibility of realizing low-cost mm-Wave antennas using inkjet printing of silver nano-particles. It is widely spread that fabrication of mm-Wave antennas and microwave circuits using the typical (deposit/pattern/etch) scheme is a challenging and costly process, due to the strict limitations on permissible tolerances. Such fabrication technique becomes even more challenging when dealing with flexible substrate materials, such as liquid crystal polymers. On the other hand, inkjet printing of conductive inks managed to form an emerging fabrication technology that has gained lots of attention over the last few years. Such process allows the deposition of conductive particles directly at the desired location on a substrate of interest, without need for mask productions, alignments, or etching. This means the inkjet printing of conductive materials could present the future of environment-friendly low-cost rapid manufacturing of RF circuits and antennas.
Shape retrieval using 3D Zernike descriptors

We advocate the usage of 3D Zernike invariants as descriptors for 3D shape retrieval. The basis polynomials of this representation facilitate computation of invariants under rotation, translation and scaling. Some theoretical results have already been summarized in the past from the aspect of pattern recognition and shape analysis. We provide practical analysis of these invariants along with algorithms and computational details. Furthermore, we give a detailed discussion on influence of the algorithm parameters like the conversion into a volumetric function, number of utilized coefficients, etc. As is revealed by our study, the 3D Zernike descriptors are natural extensions of recently introduced spherical harmonics based descriptors. We conduct a comparison of 3D Zernike descriptors against these regarding computational aspects and shape retrieval performance using several quality measures and based on experiments on the Princeton Shape Benchmark.
A dynamic risk-based access control architecture for cloud computing

Cloud computing is a distributed computing model that still faces problems. New ideas emerge to take advantage of its features and among the research challenges found in the cloud, we can highlight Identity and Access Management. The main problems of the application of access control in the cloud are the necessary flexibility and scalability to support a large number of users and resources in a dynamic and heterogeneous environment, with collaboration and information sharing needs. This paper proposes the use of risk-based dynamic access control for cloud computing. The proposal is presented as an access control model based on an extension of the XACML standard with three new components: the Risk Engine, the Risk Quantification Web Services and the Risk Policies. The risk policies present a method to describe risk metrics and their quantification, using local or remote functions. The risk policies allow users and cloud service providers to define how to handle risk-based access control for their resources, using different quantification and aggregation methods. The model reaches the access decision based on a combination of XACML decisions and risk analysis. A prototype of the model is implemented, showing it has enough expressivity to describe the models of related work. In the experimental results, the prototype takes between 2 and 6 milliseconds to reach access decisions using a risk policy. A discussion on the security aspects of the model is also presented.
Vulnerability Assessment of OAuth Implementations in Android Applications

Enforcing security on various implementations of OAuth in Android apps should consider a wide range of issues comprehensively. OAuth implementations in Android apps differ from the recommended specification due to the provider and platform factors, and the varied implementations often become vulnerable. Current vulnerability assessments on these OAuth implementations are ad hoc and lack a systematic manner. As a result, insecure OAuth implementations are still widely used and the situation is far from optimistic in many mobile app ecosystems.
 To address this problem, we propose a systematic vulnerability assessment framework for OAuth implementations on Android platform. Different from traditional OAuth security analyses that are experiential with a restrictive three-party model, our proposed framework utilizes an systematic security assessing methodology that adopts a five-party, three-stage model to detect typical vulnerabilities of popular OAuth implementations in Android apps. Based on this framework, a comprehensive investigation on vulnerable OAuth implementations is conducted at the level of an entire mobile app ecosystem. The investigation studies the Chinese mainland mobile app markets (e.g., Baidu App Store, Tencent, Anzhi) that covers 15 mainstream OAuth service providers. Top 100 relevant relying party apps (RP apps) are thoroughly assessed to detect vulnerable OAuth implementations, and we further perform an empirical study of over 4,000 apps to validate how frequently developers misuse the OAuth protocol. The results demonstrate that 86.2% of the apps incorporating OAuth services are vulnerable, and this ratio of Chinese mainland Android app market is much higher than that (58.7%) of Google Play.
ARMin II - 7 DoF rehabilitation robot: mechanics and kinematics

Task-oriented repetitive movements can improve motor recovery in patients with neurological or orthopaedic lesions. The application of robotics can serve to assist, enhance, evaluate, and document neurological and orthopaedic rehabilitation. ARMin II is the second prototype of a robot for arm therapy applicable to the training of activities of daily living. ARMin II has a semi-exoskeletal structure with seven active degrees of freedom (two of them coupled), five adjustable segments to fit in with different patient sizes, and is equipped with position and force sensors. The mechanical structure, the actuators and the sensors of the robot are optimized for patient-cooperative control strategies based on impedance and admittance architectures. This paper describes the mechanical structure and kinematics of ARMin II.
Miniature IMU/INS with optimally fused low drift MEMS gyro and accelerometers for applications in GPS-denied environments

TAI's multi-sensor fusion technology is accelerating the development of accurate MEMS sensor-based inertial navigation in situations where GPS does not operate reliably (GPS-denied environments). TAI has demonstrated that one inertial device per axis is not sufficient to produce low drift errors for long term accuracy needed for GPS-denied applications. TAI's technology uses arrays of off-the-shelf MEMS inertial sensors to create an inertial measurement unit (IMU) suitable for inertial navigation systems (INS) that require only occasional GPS updates. Compared to fiber optics gyros, properly combined MEMS gyro arrays are lower cost, fit into smaller volume, use less power and have equal or better performance. The patents TAI holds address this development for both gyro and accelerometer arrays. Existing inertial measurement units based on such array combinations, the backbone of TAI's inertial navigation system (INS) design, have demonstrated approximately 100 times lower sensor drift error to support very accurate angular rates, very accurate position measurements, and very low angle error for long durations. TAI's newest, fourth generation, product occupies small volume, has low weight, and consumes little power. The complete assembly can be potted in a protective sheath to form a rugged standalone product. An external exoskeleton case protects the electronic assembly for munitions and UAV applications. TAI's IMU/INS will provide the user with accurate real-time navigation information in difficult situations where GPS is not reliable. The key to such accurate performance is to achieve low sensor drift errors. The INS responds to quick movements without introducing delays while sharply reducing sensor drift errors that result in significant navigation errors. Discussed in the paper are physical characteristics of the IMU, an overview of the system design, TAI's systematic approach to drift reduction and some early results of applying a sigma point Kalman filter to sustain low gyro drift.
Fast and Approximate Stream Mining of Quantiles and Frequencies Using Graphics Processors

We present algorithms for fast quantile and frequency estimation in large data streams using graphics processors (GPUs). We exploit the high computation power and memory bandwidth of graphics processors and present a new sorting algorithm that performs rasterization operations on the GPUs. We use sorting as the main computational component for histogram approximation and construction of ε-approximate quantile and frequency summaries. Our algorithms for numerical statistics computation on data streams are deterministic, applicable to fixed or variable-sized sliding windows and use a limited memory footprint. We use GPU as a co-processor and minimize the data transmission between the CPU and GPU by taking into account the low bus bandwidth. We implemented our algorithms on a PC with a NVIDIA GeForce FX 6800 Ultra GPU and a 3.4 GHz Pentium IV CPU and applied them to large data streams consisting of more than 100 million values. We also compared the performance of our GPU-based algorithms with optimized implementations of prior CPU-based algorithms. Overall, our results demonstrate that the graphics processors available on a commodity computer system are efficient stream-processor and useful co-processors for mining data streams.
Improving network security monitoring for industrial control systems

Programmable Logic Controller (PLC) technology plays an important role in the automation architectures of several critical infrastructures such as Industrial Control Systems (ICS), controlling equipment in contexts such as chemical processes, factory lines, power production plants or power distribution grids, just to mention a few examples. Despite their importance, PLCs constitute one of the weakest links in ICS security, frequently due to reasons such as the absence of secure communication mechanisms, authenticated access or system integrity checks. While events such as the Stuxnet worm have raised awareness for this problem, industry has slowly reacted, either due to reliability or cost concerns. This paper introduces the Shadow Security Unit, a low-cost device deployed in parallel with a PLC or Remote Terminal Unit (RTU), being capable of transparently intercepting its communications control channels and physical process I/O lines to continuously assess its security and operational status. The proposed device does not require significant changes to the existing control network, being able to work in standalone or integrated within an ICS protection framework.
Efficient Multi-objective Neural Architecture Search via Lamarckian Evolution

Neural Architecture Search aims at automatically finding neural architectures that are competitive with architectures designed by human experts. While recent approaches have achieved state-of-the-art predictive performance for image recognition, they are problematic under resource constraints for two reasons: (1) the neural architectures found are solely optimized for high predictive performance, without penalizing excessive resource consumption; (2) most architecture search methods require vast computational resources. We address the first shortcoming by proposing LEMONADE, an evolutionary algorithm for multi-objective architecture search that allows approximating the entire Pareto-front of architectures under multiple objectives, such as predictive performance and number of parameters, in a single run of the method. We address the second shortcoming by proposing a Lamarckian inheritance mechanism for LEMONADE which generates children networks that are warmstarted with the predictive performance of their trained parents. This is accomplished by using (approximate) network morphism operators for generating children. The combination of these two contributions allows finding models that are on par or even outperform both hand-crafted as well as automatically-designed networks.
Real-World CO 2 Impacts of Traffic Congestion

Transportation plays a significant role in carbon dioxide (CO2) emissions, accounting for approximately a third of the United States’ inventory. In order to reduce CO2 emissions in the future, transportation policy makers are looking to make vehicles more efficient and increasing the use of carbon-neutral alternative fuels. In addition, CO2 emissions can be lowered by improving traffic operations, specifically through the reduction of traffic congestion. This paper examines traffic congestion and its impact on CO2 emissions using detailed energy and emission models and linking them to real-world driving patterns and traffic conditions. Using a typical traffic condition in Southern California as example, it has been found that CO2 emissions can be reduced by up to almost 20% through three different strategies: 1) congestion mitigation strategies that reduce severe congestion, allowing traffic to flow at better speeds; 2) speed management techniques that reduce excessively high free-flow speeds to more moderate conditions; and 3) shock wave suppression techniques that eliminate the acceleration/deceleration events associated with stop-and-go traffic that exists during congested conditions. Barth/Boriboonsomsin 3
Robust Hand Gestural Interaction for Smartphone Based AR/VR Applications

The future of user interfaces will be dominated by hand gestures. In this paper, we explore an intuitive hand gesture based interaction for smartphones having a limited computational capability. To this end, we present an efficient algorithm for gesture recognition with First Person View (FPV), which focuses on recognizing a four swipe model (Left, Right, Up and Down) for smartphones through single monocular camera vision. This can be used with frugal AR/VR devices such as Google Cardboard1 andWearality2 in building AR/VR based automation systems for large scale deployments, by providing a touch-less interface and real-time performance. We take into account multiple cues including palm color, hand contour segmentation, and motion tracking, which effectively deals with FPV constraints put forward by a wearable. We also provide comparisons of swipe detection with the existing methods under the same limitations. We demonstrate that our method outperforms both in terms of gesture recognition accuracy and computational time.
A MIMO System With Multifunctional Reconfigurable Antennas

A multiple-input-multiple-output (MIMO) system equipped with a new class of antenna arrays, henceforth referred to as multifunction reconfigurable antenna arrays (MRAAs), is investigated. The elements of MRAA, i.e., multifunction reconfigurable antennas (MRAs) presented in this work are capable of dynamically changing the sense of polarization of the radiated field thereby providing two reconfigurable modes of operation, i.e., polarization diversity and space diversity. The transmission signaling scheme can also be switched between transmit diversity (TD) and spatial multiplexing (SM). The results show that the reconfigurable modes of operation of an MRAA used in conjunction with adaptive space-time modulation techniques provide additional degrees of freedom to the current adaptive MIMO systems, resulting in more robust system in terms of quality, capacity and reliability. A performance gain up to 30 dB is possible with the proposed system over conventional fixed antenna MIMO systems depending on the channel conditions
Linear discriminant analysis for the small sample size problem: an overview

Dimensionality reduction is an important aspect in the pattern classification literature, and linear discriminant analysis (LDA) is one of the most widely studied dimensionality reduction technique. The application of variants of LDA technique for solving small sample size (SSS) problem can be found in many research areas e.g. face recognition, bioinformatics, text recognition, etc. The improvement of the performance of variants of LDA technique has great potential in various fields of research. In this paper, we present an overview of these methods. We covered the type, characteristics and taxonomy of these methods which can overcome SSS problem. We have also highlighted some important datasets and software/ packages.
Neural Network for Heterogeneous Annotations

Multiple treebanks annotated under heterogeneous standards give rise to the research question of best utilizing multiple resources for improving statistical models. Prior research has focused on discrete models, leveraging stacking and multi-view learning to address the problem. In this paper, we empirically investigate heterogeneous annotations using neural network models, building a neural network counterpart to discrete stacking and multiview learning, respectively, finding that neural models have their unique advantages thanks to the freedom from manual feature engineering. Neural model achieves not only better accuracy improvements, but also an order of magnitude faster speed compared to its discrete baseline, adding little time cost compared to a neural model trained on a single treebank.
Bridging the gap: automated steady scaffoldings for 3D printing

Fused Filament Fabrication (FFF) is the process of 3D printing objects from melted plastic filament. The hot plastic exits a nozzle and fuses with the part just below, adding a layer of material to the object being formed. However, filament can only be deposited on top of an existing surface. Therefore, overhangs require a disposable support structure to be printed, temporarily supporting the threads of plastic that would otherwise hang in empty space.
 Existing techniques for support generation fall into two categories: The first allow for very reliable prints by enclosing the bottom of the object in a dense structure, at the expense of increased material usage and build times. The second generate thin hierarchical structures connecting to the surface in a sparse number of points. This uses less material, at the expense of reliability: the part might become unstable, the structure itself may become difficult to print, the bottom surface quality degrades. The user therefore has to correct the structure and its parameters for each new object.
 We propose to exploit the ability of FFF printers to print bridges across gaps. Since bridges are always supported by pillars at their extremities, they are both stronger and more stable than hierarchical tree structures. Our technique first selects the points to support based on overhang and part stability during the entire print process. It then optimizes for a printable scaffolding composed of bridges and vertical pillars, supporting all points. The result is an automated support generation technique using little material while ensuring fine surface quality and stability during the printing process.
Dark web forums portal: Searching and analyzing jihadist forums

With the advent of Web 2.0, the Web is acting as a platform which enables end-user content generation. As a major type of social media in Web 2.0, Web forums facilitate intensive interactions among participants. International Jihadist groups often use Web forums to promote violence and distribute propaganda materials. These Dark Web forums are heterogeneous and widely distributed. Therefore, how to access and analyze the forum messages and interactions among participants is becoming an issue. This paper presents a general framework for Web forum data integration. Specifically, a Web-based knowledge portal, the Dark Web Forums Portal, is built based on the framework. The portal incorporates the data collected from different international Jihadist forums and provides several important analysis functions, including forum browsing and searching (in single forum and across multiple forums), forum statistics analysis, multilingual translation, and social network visualization. Preliminary results of our user study show that the Dark Web Forums Portal helps users locate information quickly and effectively. Users found the forum statistics analysis, multilingual translation, and social network visualization functions of the portal to be particularly valuable.
A Unified Attribute-Based Access Control Model Covering DAC, MAC and RBAC

Recently, there has been considerable interest in attribute based access control (ABAC) to overcome the limitations of the dominant access control models (i.e, discretionary-DAC, mandatory-MAC and role based-RBAC) while unifying their advantages. Although some proposals for ABAC have been published, and even implemented and standardized, there is no consensus on precisely what is meant by ABAC or the required features of ABAC. There is no widely accepted ABAC model as there are for DAC, MAC and RBAC. This paper takes a step towards this end by constructing an ABAC model that has “just sufficient” features to be “easily and naturally” configured to do DAC, MAC and RBAC. For this purpose we understand DAC to mean owner-controlled access control lists, MAC to mean lattice-based access control with tranquility and RBAC to mean flat and hierarchical RBAC. Our central contribution is to take a first cut at establishing formal connections between the three successful classical models and desired ABAC models.
Energy Cloud: Real-Time Cloud-Native Energy Management System to Monitor and Analyze Energy Consumption in Multiple Industrial Sites

Industrial organizations use Energy Management Systems (EMS) to monitor, control, and optimize their energy consumption. Industrial EMS are complex and expensive systems due to the unique requirements of performance, reliability, and interoperability. Moreover, industry is facing challenges with current EMS implementations such as cross-site monitoring of energy consumption and CO2 emissions, integration between energy and production data, and meaningful energy efficiency benchmarking. Additionally, big data has emerged because of recent advances in field instrumentation that led to the generation of large quantities of machine data, with much more detail and higher sampling rates. This created a challenge for real-time analytics. In order to address all these needs and challenges, we propose a cloud-native industrial EMS solution with cloud computing capabilities. Through this innovative approach we expect to generate useful knowledge in a shorter time period, enabling organizations to react quicker to changes of events and detect hidden patterns that compromise efficiency.