Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges of Specification Mining-Based Test Oracle for Cyber-Physical Systems

Test oracle problem is considered as a major challenge in software testing. Specification mining techniques are shown to be effective to tackle test oracle problem in software systems. In addition, modern systems such as Cyber-Physical Systems (CPSes) have special constraints that should be satisfied when deriving the test oracles for these systems. However, comparing different specification mining techniques for CPS applications is challenging, because no common ground to assess the effectiveness of such techniques has been established yet. In this survey, our contribution is two folded: First, we analyze the CPS constraints from the test oracle point of view, and present a framework of requirements representing six essential criteria for evaluating to which extent specification miners satisfy CPS constraints. Secondly, we review the literature for the specification mining techniques, and use our framework to compare the effectiveness of various static and dynamic analysis-based specification mining techniques, and to discuss their respective advantages and disadvantages,
Dynamic flow redirecton scheme for enhancing control plane robustness in SDN

In SDN, the controller is the core and is responsible for processing all flow requests of the network switches. However, due to the sudden occurrence and unbalanced distribution of flows in the network, it is likely that some controllers suffer workload that is far heavier than their load capacity, which leads to the failure of the controller and further leads to the paralysis of the entire network. To solve this problem, we propose a dynamic flow redirection scheme (DFR) to prevent network crash. We describe the phenomenon of controller failure caused by numerous flow requests. The flow redirection is formalized as a multi-objective optimization problem and constrained by flow table and bandwidth. We prove that the problem is NP-hard. We solve this problem with the dynamic flow redirection approach (DFR). First, state detection module detects whether the current flow requests will exceed the controller load. The Flow Redirection Assignment Module then computes the redirect path for the redundant flow request. Finally, Rule Dispense issues the flow rules to the corresponding switches. Simulation results show that DFR reduces network latency and reduces the overload probability of controllers by at least 3 times.
Event-based PID controller fully realized in neuromorphic hardware: a one DoF study

Spiking Neuronal Networks (SNNs) realized in neuromorphic hardware lead to low-power and low-latency neuronal computing architectures. Neuromorphic computing systems are most efficient when all of perception, decision making, and motor control are seamlessly integrated into a single neuronal architecture that can be realized on the neuromorphic hardware. Many neuronal network architectures address the perception tasks, while work on neuronal motor controllers is scarce. Here, we present an improved implementation of a neuromorphic PID controller. The controller was realized on Intel’s neuromorphic research chip Loihi and its performance tested on a drone, constrained to rotate on a single axis. The SNN controller is built using neuronal populations, in which a single spike carries information about sensed and control signals. Neuronal arrays perform computation on such sparse representations to calculate the proportional, derivative, and integral terms. The SNN PID controller is compared to a PID controller, implemented in software, and achieves a comparable performance, paving the way to a fully neuromorphic system in which perception, planning, and control are realized in an on-chip SNN.
Multi-Party Private Set Intersection in Vertical Federated Learning

Vertical federated learning (VFL) is a privacy-preserving machine learning framework in which the training dataset is vertically partitioned and distributed over multiple parties, i.e., for each sample each party only possesses some attributes of it. In this paper we address the problem of computing private set intersection (PSI) in VLF, in which a private set denotes the data possessed by a party satisfying some distinguishing constraint. This problem actually asks how the parties jointly compute the common IDs of their private sets, which plays a key role in many learning tasks such as Decision Tree Learning. Currently all known PSI protocols, to our knowledge, either involve expensive cryptographic operations, or are designed for the two-party scenario originally which will leak privacy-sensitive information in multi-party scenario if applied to each pair of parties gradually. In this paper we propose a new multi-party PSI protocol in VFL, which can even handle the case that some parties drop out in the running of the protocol. Our protocol achieves the security that any coalition of corrupted parties, which number is less than a threshold, cannot learn any secret information of honest parties, thus realizing the goal of preserving the privacy of the involved parties. Moreover, it only relies on light cryptographic primitives (i.e. PRGs) and thus works more efficiently compared to the known protocols, especially when the sample number of dataset gets larger and larger. Our starting point to solve the PSI problem in VFL is to reduce it to computing the AND operation of multiple bit-vectors, each held by one party, which are used to identify parties' private sets in their data. Then our main technical contribution is to present an efficient protocol for summing up these vectors, called MulSUM, and then adapt it to a desired protocol, called MulAND, to compute the AND of these vectors, which result actually identifies the intersection of private sets of all (online) parties, thus accomplishing the PSI issue.
A Multi-link In-pipe Inspection Robot Composed of Active and Passive Compliant Joints

AIRo-5.1 an in-pipe inspection robot comprised of two passive compliant joints and a single active compliant joint that is driven by a series elastic actuator (SEA) is presented in the course of this study. As an aid in pipeline maintenance, AIRo-5.1 controls joint angles and the torque of middle joints, to enable them to adapt to bend, branch, vertical pipes, and slippery surfaces. To sense the joint torques, an improved durable polyurethane rubber spring was installed. To smoothly pass through T-branches, the angle trajectory of middle joints was calculated based on the pipe geometry and thus, was interpolated using a cosine curve. Experiments to verify robot performance in bent and T-branch pipes, its joint angle and torque control was conducted.
Joint User Association and Power-Bandwidth Allocation in Heterogeneous Cellular Networks

Transmission delay minimization can be considered as an ideal goal for any data transmission system. Regarding the radio resources restriction in cellular networks (e.g. transmit power and bandwidth of base stations (BSs)) delay improvement is taken into account as a complex network optimization problem. Here, the decision process of user association to multiple BSs and traffic load balancing is proposed which improves the resource utilization and establishes the throughput required by each network user with the minimum total average transmission delay. The network to be investigated is heterogeneous cellular network. The average delay is formulated and an optimization problem is proposed to minimize the delay subject to power and bandwidth limitation and also the rate stability condition. This problem is originally nonconvex and a combination of difference of convex method and inner approximation is utilized to convert the problem to a convex form. Various types of user distribution are examined in simulation and the results show significant decrease in system delay average compared to well-known user association approaches such as minimum distance and maximum received signal straight criteria.
Identification of a Human Hand Kinematics by Measuring and Merging of Nail-Based Finger Motions

A method to identify the kinematics model of a human hand that less suffers from the skin artifact is proposed based on a fact that the movements of nails with respect to the corresponding fingertip bones are much smaller than that of skin. It consists of two stages. In the first (individual) stage, the most likely combination of joint assignments and angles of each finger is identified through a dual-phase least squares method (LSM), where the joint angles are estimated in the inner LSM and the joint assignments in the outer LSM, from the movement of the hand dorsum with respect to the base coordinate frame attached to each nail. In the second (merging) stage, kinematic models of each finger are merged so as to compromise the estimated movements of the hand dorsum by them also through the dual-phase LSM. It is shown that the identified joint assignments have an advantage over several existing anthropomorphic robot hands based on the distribution of pinchability (DOP), which is also proposed in this paper as a novel index to evaluate the ability of in-hand manipulation.
3D Coating Self-Assembly for Modular Robotic Scaffolds

This paper addresses the self-reconfiguration problem in large-scale modular robots for the purpose of shape formation for object representation. It aims to show that this process can be accelerated without compromising on the visual aspect of the final object, by creating an internal skeleton of the shape using the previously introduced sandboxing and scaffolding techniques, and then coating this skeleton with a layer of modules for higher visual fidelity.We discuss the challenges of the coating problem, introduce a basic method for constructing the coating of a scaffold layer by layer, and show that even with a straightforward algorithm, our scaffolding and coating combo uses much fewer modules than dense shapes and offers attractive reconfiguration times. Finally, we show that it could be a strong alternative to the construction of dense shapes using traditional self-reconfiguration algorithms.
Abrasion Status Prediction with BP Neural Network Based on an Intelligent Tire System

The active safety technology of vehicles has become a hot research topic nowadays, among which the intelligent tire is an important field. The existing tire pressure monitoring system (TPMS) however, cannot provide a comprehensible supervision for the tires. This paper proposed an intelligent tire information system, which can estimate the tire abrasion depth, based on triaxial accelerometer data and strain gauge data. The system consists of different sensors in the data acquisition section, a controlling chip in the car terminal and the display section on mobile devices. Based on the data analysis from the sensors and multiple useful features extracted from the data, a BP neural network is established to training an abrasion depth prediction model in MATLAB. At last, we conduct experiments to verify our models with 943 collected data and compare the model with the multivariable linear regression model. The absolute errors of randomly chosen testcases can all keep within ±0.5mm and about 80% of them lies in the range of ±0.2mm, which shows the efficiency and reliability of our system and prediction model.
A Recommendation for a Systems Engineering Process and System Architecture for UAS

The development of Unmanned Aircraft Systems (UAS) is complex and usually expensive, as commercial tools are often used, adapted from the development of (small) manned aircraft. To be sure that the authorities will approve the to be developed UAS, some steps within the Systems Engineering (SE) process are required, which are not yet defined in a UAS specific standard. This paper outlines a SE process to develop UAS with open-source tools and shows UAS specific tasks to be thought of, like SORA. The recommended SE process and a system architecture approach should lower the entrance level for small and medium-sized enterprises to develop their own UAS. Besides, frameworks, like ROS or OpenCV, that should be considered working with are briefly explained.
Lightweight Digital Signature Solution to Defend Micro Aerial Vehicles Against Man-In-The-Middle Attack

Micro aerial vehicles, a.k. a. drones, have become an integral part of a variety of civilian and military application domains, including but not limited to aerial surveying and mapping, aerial surveillance and security, aerial inspection of infrastructure, and aerial delivery. Meanwhile, the security and privacy of drones are gaining significant attention due to both financial and strategic information and value involved in aerial applications. Due to the lack of security features in communication protocols, an adversary can easily interfere with on-going communications or even seize the control of drone. In this paper, we propose a lightweight digital signature protocol, also referred to as DroneSig, to protect drones from man-in-the-middle attack, where an adversary eavesdrops the communications between Ground Control Station (GCS) and drone, and impersonates the GCS and sends fake commands to terminate the ongoing mission or even take control over the drone. The basic idea of the DroneSig is that the drone will only execute the new command after validating the received digital signature from the GCS, proving that the new command message is coming from the authenticated GCS. If the validation of digital signature fails, the new command is rejected immediately and the Return-to-Launch (RTL) mode is initiated and forces the drone to return to takeoff position. We conduct extensive simulation experiments for performance evaluation and comparison with existing Advanced Encryption Standard (AES), Data Encryption Standard (DES), and Triple DES (3DES). The simulation results show that the proposed DroneSig can achieve better performance in terms of energy consumption, computation time, CPU cycle, memory usage, and code size, indicating a viable and competitive approach defending drones against man-in-the-middle attack.
Malware Detection Based on Term Frequency Analysis of GPRs Features

Recently, low-level hardware micro-architecture features are widely used for malware detection, but they always have redundant information, which will inevitably affect malware detection. To address the above problem, this paper proposed a novel dynamic analysis method to detect malware. The feature matrices are first extracted from the General-Purpose Registers (GPRs) that contain a large amount of valuable but redundant information. To reduce the feature dimension, Term Frequency-Inverse Document Frequency (TF-IDF) technique is then used to select the discriminative information from feature matrices. With the selected features, this paper also designs an ensemble learning model for malware detection. Experimental results show that the proposed method performs better than other state-of-art methods.
Active Fault Tolerant Control System Design for Hydraulic Manipulator With Internal Leakage Faults Based on Disturbance Observer and Online Adaptive Identification

In this paper, an active fault-tolerant control (FTC) system design is proposed for an n-degree-of-freedom (n-DOF) hydraulic manipulator with internal leakage faults and mismatched/matched lumped disturbances. A pair of matched and mismatched disturbance observers (DOBs) is proposed to simultaneously estimate and compensate for the effects of matched/mismatched disturbances on the control system in healthy conditions. The fault detection is achieved when the estimated matched disturbance is larger than a threshold. After that, a novel control reconfiguration law is designed to switch from a normal controller to a fault-tolerant controller with an online identification algorithm based on an adaptive mechanism. The proposed active FTC guarantees the position tracking performance in not only single-fault but also simultaneous-faults conditions. Moreover, the problem of uniting disturbance-observer-based control for external disturbance and adaptive control for parametric uncertainty is solved in a novel approach. Simulation results are conducted in a two-degree-of-freedom hydraulic leg prototype, which verifies the effectiveness of the proposed method.
A Security Analysis of Blockchain-Based Did Services

Decentralized identifiers (DID) has shown great potential for sharing user identities across different domains and services without compromising user privacy. DID is designed to enable the minimum disclosure of the proof from a user’s credentials on a need-to-know basis with a contextualized delegation. At first glance, DID appears to be well-suited for this purpose. However, the overall security of DID has not been thoroughly examined. In this paper, we systemically explore key components of DID systems and analyze their possible vulnerabilities when deployed. First, we analyze the data flow between DID system components and analyze possible security threats. Next, we carefully identify potential security threats over seven different DID functional domains, ranging from user wallet to universal resolver. Lastly, we discuss the possible countermeasures against the security threats we identified.
Structure of glyoxal dihydrazone and synthesis, characterization, and iodine doping of unsubstituted polyazine

La structure cristalline de la dihydrazone est determinee. Sa polycondensation en solution avec le glyoxal donne la polyazine (-N=CH-CH=N-) n dont la structure est etudiee par spectroscopie IR et RMN. La double liaison C=N du polymere est E et la conformation de la liaison carbone-carbone comme dans le monomere; 15 % des motifs structuraux portent des defauts structuraux. La conductivite du polymere dope atteint 10 −6 Ω −1 •cm −1
ConFlo III – an interface for high precision δ13C and δ15N analysis with an extended dynamic range

A newly developed interface coupling a CHN combustion device (elemental analyser 'EA') to an isotope ratio mass spectrometer is described and evaluated. The purpose of the device is to extend the dynamic range of delta(13)C and delta(15)N analysis from less than 2 orders of magnitude to more than 3 orders of magnitude. Carbon isotope ratio measurements of atropine as a model compound have been performed analysing between 1 µg to 5 mg C with acceptable to excellent precision (0.6 to 0.06 per thousand, delta-notation). The correction due to the blank signal is critical for sample amounts smaller than 4 µg C. The maximum sample weight is determined by the combustion capacity of the EA. Larger sample amounts are measured using dilution of a small part of the EA effluent with helium. The dilution mechanism works virtually free of isotope fractionation. Copyright 1999 John Wiley & Sons, Ltd.
Transformations of the Micro-Domain Structure of Polyimide Films during Thermally Induced Chemical Conversion: Characterization via Thermodynamics of Irreversible Processes

The transformation of the micro-domain structure of polyimide films during thermally induced chemical conversion from the polyamic acid prepared from 3,3‘,4,4‘-diphenyl tetracarboxylic dianhydride and p-phenylene diamine, also in the presence of triphenyl phosphate, is studied with a transmission electron microscope. The surface morphology of the films is typified by a broad size distribution of micro-domains that depends on the history of the sample treatment. To describe these observations thermodynamics of irreversible processes is used with the special feature that very slowly running stationary nonequilibrium states are considered to operate as temporary states of reference. In terms of an increment model a generalized version of the law of mass action comes out that holds under stationary nonequilibrium conditions. The observed stationary nonequilibrium patterns developed at elevated temperatures under different conditions (also in the presence of a stabilizer) are different due to process controlle.
Highly Selective Perchlorate Membrane Electrode Based on Cobalt(III) Schiff Base as a Neutral Carrier

A highly selective poly(vinyl chloride) (PVC) membrane electrode based on Co(III)-Schiff base [Co(5-NO2Salen)(PBu3)]ClO4•H2O (where 5-NO2-SalenH＝bis(5-nitrosalycilaldehyde)ethylenediamine) as a new carrier for construction of perchlorate-selective electrode by incorporating the membrane ingredients on the surface of a graphite electrodes has been reported. The proposed electrode possesses a very wide Nernestian potential linear range to perchlorate from 1.0×10 －6 to 5.0×10 －1
Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive

Long-term record of fine spatial resolution remote sensing datasets is critical for monitoring and understanding global environmental change, especially with regard to fine scale processes. However, existing freely available global land surface observations are limited by medium to coarse resolutions (e.g., 30 m Landsat) or short time spans (e.g., five years for 10 m Sentinel-2). Here we developed a feature-level data fusion framework using a generative adversarial network (GAN), a deep learning technique, to leverage the overlapping Landsat and Sentinel-2 observations during 2016–2019, and reconstruct 10 m Sentinel-2 like imagery from 30 m historical Landsat archives. Our tests with both simulated data and actual Landsat/Sentinel-2 imagery showed that the GAN-based fusion method could accurately reconstruct synthetic Landsat data at an effective resolution very close to that of the real Sentinel-2 observations. We applied the GAN-based model to two dynamic systems: (1) land over dynamics including phenology change, cropping rotation, and water inundation; and (2) human landscape changes such as airport construction, coastal expansion, and urbanization, via historical reconstruction of 10 m Landsat observations from 1985 to 2018. The resulting comparison further validated the robustness and efficiency of our proposed framework. Our pilot study demonstrated the promise of transforming 30 m historical Landsat data into a 10 m Sentinel-2-like archive with advanced data fusion. This will enhance Landsat and Sentinel-2 data science, facilitate higher resolution land cover and land use monitoring, and global change research.
Virus Taxonomy The ICTV Report on Virus Classification and Taxon Nomenclature Secoviridae Chapter Secoviridae

Members of the family Secoviridae are non-enveloped viruses with monoor bipartite (RNA-1 and RNA-2) linear positive-sense ssRNA genomes of 9 to 13.7 kilobases in total (Table 1.Secoviridae). Secoviruses are related to picornaviruses and are classified in the order Picornavirales. The majority of known members infect dicotyledonous plants and many are important plant pathogens (e.g. grapevine fanleaf virus and rice tungro spherical virus).
Impact of Ketogenic Diet on Body Composition during Resistance Training among Untrained Individuals

Twenty untrained participants were randomly assigned to the RE+RT and KD+RT as control and experimental groups, respectively. Sixty to ninety minutes of diversified resistance exercise were performed by both groups, three sessions weekly, and diet was self-administered with a recommended daily energy and protein intakes. Body composition was measured using a Bioelectrical Impedance Analyzer. One-way Analysis of Covariance (ANCOVA) was applied to analyze the data.