Reviewerly/exHarmony huggingface dataset:
Robust Multi-Robot Trajectory Optimization Using Alternating Direction Method of Multiplier

We propose a variant of alternating direction method of multiplier (ADMM) to solve constrained trajectory optimization problems. Our ADMM framework breaks a joint optimization into small sub-problems, leading to a low iteration cost and decentralized parameter updates. Starting from a collision-free initial trajectory, our method inherits the theoretical properties of primal interior point method (P-IPM), i.e., guaranteed collision avoidance and homotopy preservation throughout optimization, while being orders of magnitude faster. We have analyzed the convergence and evaluated our method for time-optimal multi-UAV trajectory optimizations and simultaneous goal-reaching of multiple robot arms, where we take into consider kinematics-, dynamics-limits, and homotopy-preserving collision constraints. Our method highlights an order of magnitude's speedup, while generating trajectories of comparable qualities as state-of-the-art P-IPM solver.
Reinforcement Learning-Based Composite Controller for Cable-Driven Parallel Suspension System at High Angles of Attack

This paper investigates an intelligent method for the motion control of a cable-driven parallel suspension system (CDPSS) in wind tunnel tests, especially at high angles of attack, which is characterized by unsteady and nonlinear aerodynamics.Considering the modeling uncertainties and the complex aerodynamic interference, a composite controller that combines deep deterministic policy gradient (DDPG) and computed-torque is proposed to improve the control performance.The tasks at hand consist in the construction of the training environment based on the dynamic equations and the Markov Decision Process (MDP) design.The supplementary computed-torque control is used to enhance the learning rate of the agent.Then a well-trained agent is applied in the high angles of attack maneuvers control with different examples, including single-DOF and multi-DOF coupled motion.The simulation results show the controller could fulfill the training tasks efficiently, and it turns out to be robust and maintain strong generalization ability despite handling the unlearned tasks.
Context modeling for cyber‐physical systems

Abstract When developing cyber‐physical systems (CPS), the context is of vital importance. CPS interact with the world not only through sensing the environment and acting upon it (like embedded systems) but also by communicating with other CPS (like systems in the Internet of Things [IoT]). This means that the context interactions CPS must deal with are much greater than regular embedded or IoT systems: On the one hand, external systems and human users constrain the specific interaction among them. On the other hand, properties of these external systems, human users, and laws, regulations, or standards constrain the way the CPS must be developed. In this paper, we propose a comprehensive, ontologically grounded context modeling framework to systematically explore the problem space in which a CPS under development will operate. This allows for the systematic elicitation of requirements for the CPS, early validation and verification of its properties, and safety assessment of its context interactions at runtime.
A Method for Inversion of Electrical Parameters of Materials Based on Ray Tracing Algorithm and Stochastic Gradient Descent Method

In wireless channel deterministic models, it is important part for the setting of environmental electrical parameters. Traditional calculation methods such as genetic algorithm and the ant colony algorithm are inefficient. This paper studies the stochastic gradient descent(SGD) algorithm to solve the electrical parameters. The simulation results show that the method based on ray tracing algorithm and SGD algorithm to solve the electrical parameters of materials is feasible and efficient.
Ontology-based Software Trustworthy Requirements and Behavior Modeling

In the process of software development, people pay more and more attention to the trustworthiness attribute of software, and the trustworthiness description of software requirements has become an important part of requirements engineering. For the description of trusted requirements, most of the methods still use the description of words and graphics, and their contents are limited to the category of specific business logic, which fails to abstract some highly universal contents. Under the trend of more and more complex trusted requirements, it not only causes the increase of requirements understanding cost and the waste of resources to repeat the existing work in the requirements process, but also leads to the result that the requirements data is not easy to be mined and utilized. In order to improve this situation (purpose), this paper proposes a new model construction and description method. On the basis of previous research, this paper constructs the trusted requirements and behavior model of software based on ontology formal modeling method, and describes the differences between functions by taking the trusted concerns of software requirements as the characteristic attributes of requirements in different scenarios; Finally, the formal description is used as the data source, and the process of expecting behavior from requirements is completed according to the attributes in ontology and the random forest algorithm based on bagging idea. This method can help developers to better reuse the past experience in the process of requirements acquisition, and can more reasonably and accurately anticipate the behavior of software.
Estimating the height of a person from a video sequence

The research described in this paper focuses on a development of experimental solution, which estimates human height from a video sequence. The main requirements include portability, low hardware requirements, and accuracy. The paper includes analysis of the algorithms employed for image processing, human detection and methods for estimating height of human. The experimental software solution is implemented in Python programming language. The SSD algorithm was selected to detect the person in the image and the output was later on utilized to calculate the real human height. A calibration object in a form of ArUco marker and its known dimensions became the foundation for calculating the real human height. The experimental testing confirmed the accuracy of the developed solution to be sufficient for the real-world application. The most accurate height, with just a deviation of 0.001 meters, was estimated at a distance of 5 and 8 meters. The approach presented in this paper is also to be used as a complement by newly proposed unmanned aerial vehicle security system.
Hash Functions

In information security, hash functions are an important cryptographic tool. They have applications in a wide range of use cases, such as securing passwords in the database, securing authentication, or maintaining message integrity.
“Digitalizing” Optical Layer for The Green Computing Continuum As The Future Digital Infrastructure

This talk will review, from the perspective of photonics, the technological trends of the digital infrastructure migrating toward the “computing continuum,” where the optical network and computing infrastructure are converged. The functional block-based disaggregation (FBD) model will be introduced as a key to incorporate the optical layer switching into the future digital infrastructure.
Single-Trace Fragment Template Attack on a 32-Bit Implementation of Keccak

Template attacks model side-channel leakage information using Gaussian multivariate distributions. They have been quite successful in directly reconstructing individual bits of 8-bit parallel buses and registers from power traces. However, extending their use directly to larger word sizes, such as 32-bit buses, becomes impractical. Here we show that it is possible to use an LDA-based stochastic model to independently build templates for just byte fragments of such a word, to predict the exact values of its four member bytes, instead of only overall Hamming weights. We demonstrate this technique to reconstruct the arbitrary-length inputs of SHA3-512 and some other Keccak sponge functions implemented on a 32-bit Cortex-M4 device. The quality of these templates was high enough such that remaining errors in their predictions could be eliminated via belief propagation on a factor-graph network (SASCA). In our experiments, we already reliably recovered SHA3-512 inputs up to 719 bytes long (10 invocations of the permutation), and reconstructing even longer inputs should be just a matter of making longer recordings.
A Cross-Modality Sketch Person Re-identification Model Based on Cross-Spectrum Image Generation

Person re-identification (Re-ID) retrieval usually requires the query photo of the target person. However, in the actual crime forensics scene, it is difficult to obtain such photos. This paper studies the problem of Sketch person Re-ID, whose query image is the professional sketch of the target rather than the picture captured by the camera. It is a very challenging problem that sketch only has highly abstracted body outline information. In this paper, a cross-spectrum image generation (CSIG) method is proposed to process the input image, and then the generated image is sent to a dual-stream network model for training. The generalized-mean pooling (GMP) layer is added to the network and the domain invariant features are extracted by weighted regularization triplet (WRT) loss. We validated this method on the Sketch person Re-ID dataset, and the experimental results show that the proposed method outperforms the state-of-the-arts.
Anonymization of distribution feeder data using statistical distribution and parameter estimation approach

Distribution networks are undergoing a transition due to the rapid increase in distributed energy resources (DERs). Concerns over revealing critical infrastructure information and breaching consumer privacy have significantly hindered their development. Many applications that require data sharing are still impractical due to the lack of sufficient datasets, privacy concerns, and data access problems. To address these issues, a statistical distribution approach for parameter estimation is proposed. The statistical patterns of real distribution feeders are examined by accessing the confidential database of a local distribution network service provider (DNSP). An algorithm based on the Maximum Likelihood Estimate (MLE) is applied to estimate the statistical distribution parameters that represent the actual data. Then, these statistical distribution parameters are used to generate anonymized datasets that are realistic. A Kolmogorov–Smirnov (K-S) test is conducted to confirm the effectiveness of anonymized datasets, and results are compared with the actual feeder datasets. Validation is carried out with existing methods and comparisons are shown on the different portions of the datasets (25 percent, 50 percent, 75 percent, and 100 percent). The comparison results indicate superior performance over traditional methods, with a performance improvement ranging from 1 to 13 percent. The practical application of the method is demonstrated on the IEEE 123-node test feeder. The method achieves consistent results on voltage profiles, with a maximum difference of 0.420 percent between actual and anonymized datasets.
Inter-task Similarity Measure for Heterogeneous Tasks

A long term goal in robotics is lifelong learning, in which a robot learns to perform new tasks without human intervention. For this a promising alternative is reinforcement learning (RL), in which an agent learns how to solve sequential decision-making problems by interacting with its environment. Despite the effectiveness of RL, learning in a trial-and-error way can be infeasible in robotics applications, in which long training periods can significantly deteriorate hardware. For robots that face long sequences of tasks, transfer learning represents an alternative to reduce training time, by transferring knowledge from previously learned tasks. However, in domains such as service robotics, identifying reusable pieces of knowledge and transferring them across tasks can be challenging, due to the mismatch between the state-action spaces (heterogeneous tasks). Thus, in this article we present an inter-task similarity measure and a transfer learning method for heterogeneous tasks. Through experimental evaluations, we show that the proposed measure is able to rank tasks in a way that prioritizes those that cause a larger increment of the learning performance in a target task. Additionally, results show that for some tasks the similarity measure can be computed with few data, enabling significant speedups in the learning process by transferring knowledge at an early stage.
Application of Computer Big Data Technology in the Safety Intelligent Monitoring of High Voltage Charging of Electric Vehicles

The continuous development of new energy vehicles coincides with the concept of energy conservation and environmental protection. In order to improve the high-voltage safety of electric vehicles, the functions of the high-voltage safety monitoring system have been expanded in the computer big data technology environment. In the overall structure, the traditional high-voltage safety monitoring system for electric vehicles is integrated into the Internet of Vehicles to realize the interconnection and sharing of fault information; in terms of fault information communication means, short-range communication is also used to realize the communication between the vehicle and the vehicle, and between the vehicle and the road infrastructure. Fault information sharing, through remote communication to realize the fault information sharing between the car and the user, the car and the Internet; in the fault alarm mode, for the general fault of the high voltage safety of the electric vehicle, the driver will be prompted by the sound and light alarm and the LCD screen to prompt the driver of the fault type and Approach. The research results show that the high-voltage safety monitoring system in the computer big data technology environment is a seamless connection between the monitoring system and the Internet of Vehicles, which is beneficial to prevent safety accidents.
A K-Nearest Centroid Neighbor with attention Classifier

Among classic algorithms of data mining, the K-nearest neighbor based methods are simple and effective pattern classification algorithms. However, most KNN-based methods do not fully take into account the impact of different training sample points on classification, lead to inaccurate classification. To address this issue, we propose a scheme named Attention-based local mean K-Nearest Centroid Neighbor Classifier (ALMKNCN), combining nearest centroid neighbor with attention mechanism, the influence of each training sample on the query sample is fully considered. Given the query pattern, we first calculate the local centroid mean vector for each class, and then use the idea of attention mechanism to calculate the weight of pseudo-distance between each class and test sample. Finally, based on attention coefficient, the distances between the query sample and local mean vectors are weighted to determine the class of the query sample. Extensive experiments on UCI and KEEL data sets are carried out by comparing ALMKNCN to the state-of-art KNN-based methods. The experimental results demonstrate that the proposed ALMKNCN outperforms the related competitive KNN-based methods with more effectiveness.
Research on Privacy Protection Technology for Data Publishing

With the Internet of Things (IoT) technology development, the agent terminals are able to collect and publish richer and more valuable privacy data. If it is short of the necessary privacy protection measures, the published private data will be leaked. The core of privacy protection technology in the field of data publishing is anonymity technology, encryption technology, and distortion technology. Current privacy protection methods cannot effectively keep the balance between data privacy and availability. This paper will conduct a systematic comparative analysis of the data publishing model, attack models, data publishing privacy protection technologies as well as applications in other fields. Finally, we will summarize the current research progress and problems.
Algorithm for Constructing a Cognitive Aggregate-Stream Model of the Automatic Spacecraft Flight Control Process

The resource consumption of onboard systems (OS) largely depends on the synergetic phenomena that occur during intersystem interaction in automatic spacecraft (AS). By using these phenomena, it is possible to increase the efficiency of using existing resources, as well as to supplement them with new “synergistic” resources [1]. At the same time, synergetic phenomena can lead to premature development of the OS resource and unforeseen (non-calculated) failures and accidents [2]. For a targeted search for data on synergetic phenomena, special modeling of processes occurring on board is required. The purpose of the research was to move from the system-cybernetic model of the AS as a “black box” to a model that provides “transparency” of the AS as a “white box” for processes occurring on board [3], taking into account synergetic phenomena. The article considers a new algorithm for constructing a cognitive aggregate-flow model of the AS flight control process that meets the stated requirements.
Seeking Sigma: Time-of-the-Day Effects on the Bitcoin Network

This research investigates and tests for the presence of time-of-the-day effects on the Bitcoin network. Results indicate that NYSE trading sessions lead Bitcoin trading activity, both on the blockchain and centralised exchanges. Effects are found to have strengthened over time, however, simultaneously diminished at the weekend indicating significant exchange interactions, and that Bitcoin has developed somewhat outside its intended design parameters, and is influenced by other forces such as those originating from NYSE trading. While proponents consider Bitcoin trading to be `24/7', our findings suggest that both transaction and on-chain network activity are best described to be, at best, `12/5', presenting significant implications for traders, with regards to centralised exchange liquidity and the speed of their transaction inclusion on the blockchain. Finally, the role and influence of both algorithm and volatility traders cannot be eliminated.
Seamless Group Pre-handover Authentication Scheme for 5G High-Speed Rail Network

The rapid development of global wireless networks has promoted the innovation of 5G network application scenarios and the improvement of infrastructure. The deployment of 5G network in high-speed railway network system has important practical application prospects. Data security and user experience are significant factors to consider for 5G high-speed rail network. However, existing 5G network handover authentication mechanisms still have challenging issues in terms of security and efficiency. Considering the complexity and diversity of the future 5G high-speed rail network, this paper proposes a secure and efficient group pre-handover authentication scheme based on Mobile Relay Node (MRN). MRN with trusted execution environment assists 5G User Equipment (UE) on high-speed rail to complete the handover process before reaching the next 5G base station in the authentication and handover execution phase, so as to improve the security and handover efficiency of UE in the authentication and handover process. Therefore, this scheme can reduce handover delay and realize fast and secure group handover of high-speed UEs under the dense-deployed 5G base station. After comprehensive performance evaluation, security and communication efficiency are superior to other schemes.
On Free Velocity Cones of Narrow Passages of High-DoF Kinematic Chains

Narrow passages in free space pose great challenges to many sampling-based motion planners. In problems with high-dimensional configuration spaces (C-spaces), narrow passages in free space (C-free) can be hard to find and navigate and, sometimes, even counterintuitive. In this article, we present an algorithm to construct cones of available instantaneous velocities (“free velocity cones” or, briefly, “free cones”) on the boundary of C-free to facilitate finding and navigating narrow passages. This is accomplished by first developing free cones and associated measures of local C-space narrowness for a single rigid link. These results are then extended to kinematic chains (open and closed) of arbitrary degrees of freedom. It turns out that the degeneracy of the free cones dictates the existence of C-space narrow passages, and the locations and orientations of the links in the workspace relate intimately to the corresponding connected component of C-free. This observation leads us to a modified probabilistic roadmap (M-PRM) algorithm and a modified rapidly exploring random vine (M-RRV) algorithm of combining the enumeration of topological components and random sampling of the free cones. Experimental results from applying our algorithms to several challenging examples show that our new algorithms are more efficient than some variants of the PRM algorithms, and several variants of the rapidly exploring random tree (RRT) algorithms, such as RRT-CONNECT and RRV.
Simultaneous Measurement of Temperature and Strain in Electronic Packages Using Multiframe Super-Resolution Infrared Thermography and Digital Image Correlation

Abstract For micro-electronic components and systems, reliability under thermomechanical stress is of critical importance. Experimental characterization of hotspots and temperature gradients, which can lead to deformation in the component, relies on accurate mapping of the surface temperature. One method of noninvasively acquiring this data is through infrared (IR) thermography. However, IR thermography is often limited by the typically low resolution of such cameras. Additionally, the unique surface finish preparations required to infer physical deformation using digital image correlation (DIC) generally interfere with the ability to measure the temperature with IR thermography, which prefers a uniform high emissivity. This work introduces a one-shot technique for the simultaneous measurement of surface temperature and deformation using multiframe super-resolution-enhanced IR imaging combined with DIC analysis. Multiframe super-resolution processing uses several subpixel shifted images, interpolating the image set to extract additional information and create a single higher-resolution image. Measurement of physical deformation is incorporated using a test sample with a black background and low-emissivity speckle features, heated in a manner that induces a nonuniform temperature field and stretched to induce physical deformation. Through processing and filtering, data from the black surface regions used for surface temperature mapping are separated from the speckle features used to track deformation with DIC. This method allows DIC to be performed on the IR images, yielding a deformation field consistent with the applied tensioning. While both the low- and super-resolution data sets can be successfully processed with DIC, super-resolution helps to reduce noise in the extracted deformation fields. As for temperature measurement, using super-resolution is shown to allow for better removal of the speckle features and reduce noise, as quantified by a lower mean deviation from the spatial moving average.