Miracle-dz/UniFAR-FTUs huggingface dataset:
Vall-nut: Principled Anti-Grey box - Fuzzing

Greybox fuzzing is a widely used technique for software testing that has been adopted by practitioners and researchers to disclose a great number of vulnerabilities in various software. However, adversaries also weaponize greybox fuzzing to mine vulnerabilities for malicious intentions. This poses considerable threats to software systems. To counteract the misuse of greybox fuzzing, we propose VALL-NUT, a novel approach to harden software with properties to combat greybox fuzzing. We dissect the major strategies that facilitate the success of greybox fuzzing, and accordingly propose three types of neutralizing schemesseed queue explosion, seed attenuation, and feedback contamination. We evaluate Vall-nut against the mainstream greybox fuzzers on multiple real-world benchmark programs. The results show that Vall-nut can reduce an average of 34 % code coverage and 76% detected crashes in 24-hour tests. Moreover, we conduct comparisons with two recent studies which show Vall-nut can achieve a superior deduction of detected crashes.
Using Deep Learning for Urban Pedestrian Counting

Overcrowding in urban areas makes pedestrian travel slower and more dangerous: disease transmission and theft occur more frequently in crowds. This paper proposes a deep learning system designed to analyze traffic camera footage, measuring crowd densities at five intersections in Manhattan. The results were plotted on a mapping model in MATLAB. The system measured crowd densities in clear weather with an average accuracy of 75% to 83% during the daytime and 59% to 79 % during the nighttime. The results demonstrate the viability of a pedestrian counting system for monitoring foot traffic in urban settings.
Maintenance Operation Plan Generation Method Based on E-CARGO Model

In the field of equipment support, maintenance operations is one of the most important activities. A unified descriptive model of maintenance resources based on ontology is proposed to solve the problem that the current maintenance operation plan generation is heavily dependent on labor. The first step is to analyze the important elements and the reasons that affect the automatic and rapid generation of the maintenance operation plan. Then role-based collaboration and its environments—classes, agents, roles, groups, and objects (E-CARGO) model is used to abstract multi-objective constrained resource scheduling scenarios, which is combined with genetic algorithm to solve the optimal scheduling plan, finally the maintenance operation plan simulation optimization design system is realized. In practice, the system has been proved to be capable of generating maintenance operation plans, as well as providing a powerful tool for simulating and deducting maintenance expenses.
Open design platforms for open source product development: current state and requirements

The spread of ICT and cheap low-size production tools like 3D-printers led to the development of open design, i.e. community-based and open source development of physical products. This innovative organization of product development offers a great opportunity for continuous improvement of products as well as formidable a potential for product innovation and incubation of new businesses. However, because of a limited availability of supporting methods and tools, open design projects are still unable to compete with today s standards of industrial product design. The present article aims at providing a state of the art of existing online tools for open source product development and discusses their limitation regarding the challenges raised by what is identified as an emerging design paradigm. This is performed through the definition of an analysis grid through which existing tools have been scanned as well as a case study. It claims for further empirical research in order to describe the phenomenon from a design science perspective, to define appropriate categories and develop new specific online product data management tools.
CHIMERA: Autonomous Planning and Orchestration for Malware Deception

Cyber deception is a promising defense that can proactively mislead adversaries and enables a unique opportunity to engage with them to learn new attack tactics and techniques. Although cyber deception has been around for more than a decade, static configurations and the lack of automation made many of the existing deception techniques easily discoverable by attackers and too expensive to manage, which diminishes the value of this technology. Sophisticated Advanced Persistent Threats (APTs) are highly dynamic and thereby require a highly adaptive and embedded deception that can dynamically create honey resources and orchestrate the deception environment appropriately according to the adversary behavior in real-time. This paper presents a theoretical framework and implementation for an autonomous goal-oriented cyber deception planner, called CHIMERA, that optimizes deception decision-making. CHIMERA agents can reside in any production machine/server and automatically create and orchestrate the deception ploys to steer and mislead the malware or APT to the desired goal without human interaction. The deception ploys are dynamically composed based on the deception planning while ensuring safe yet fast deployment and orchestration of deceptive course-of-actions. We evaluated our deception framework with real APT attacks for information stealing, ransomware, Remote Access Trojans (RAT), and others. In these case studies with 4,578 real malware samples, we showed that CHIMERA’s adversary-aware dynamic deception strategies were able to effectively accomplish the deception goals within a few seconds and with minimum cost.
FLUIDS: Federated Learning with semi-supervised approach for Intrusion Detection System

In this paper, we present FLUIDS, a Federated Learning with semi-sUpervised approach for Intrusion Detection System. FLUIDS formulates the intrusion detection into a semi-supervised learning where both supervised learning (using labeled data) and unsupervised learning (no label data) are combined in a collaborative way. The combination of federated learning and semi-supervised Learning allows the solution to: better preserve the privacy, improve training and inference efficiency, achieve better accuracy, and be cheaper to deploy.
Automated Microsegmentation for Lateral Movement Prevention in Industrial Internet of Things (IIoT)

The integration of the IoT network with the Operational Technology (OT) network is increasing rapidly. However, this incorporation of IoT devices into the OT network makes the industrial control system vulnerable to various cyber threats. Hacking an IoT device at the network edge, an attacker can move laterally to compromise the main control server and manipulate the whole control system of the industrial infrastructure. In this paper, we have proposed an automated Micro-segmentation (MS) model based on Machine Learning (ML) algorithms to reduce the lateral movement of an attacker or malware. The proposed model generates the micro-segments based on network traffic and blocks the malicious traffic at each segment. We have taken UNSW-NB15 and IoTID20 datasets for our experiments. Experimental results show that after generating micro-segments and separating the normal traffic, the model limits redundant links and blocks malicious traffic. Limiting the usage of redundant links reduces the lateral movement or spreading of malware. We also considered the deterministic epidemic model to analyze the device infection rate due to lateral movement or malware propagation.
A Symmetry Evaluation Model for Lower Limb Segments During Upper Limb Loading

Weight-bearing is a common behavior, whereas a load impact on human gait symmetry is seldom considered. This study aims to propose and verify a gait symmetry detection model, by using the gait symmetry under different weight-bearing states to establish a weight-bearing symmetry classification rule, and to measure the consistency of movement through multiple gait symmetry experiments. In details, a classifier combined with statistical learning is presented to quantify the signal difference between left and right legs. The quantization result is incorporated as an index to judge the magnitude of symmetry. Meanwhile, a dynamic time warping (DTW) algorithm is introduced to calculate the signal similarity of two legs in time domain as well as another index. The symmetry trend on leg signals is also considered and described as a third index. By casting these indexes in the proposed detection model, we employ several existing nonlinear classification methods to effectively disclose the difference between legs. Moreover, the information between legs can also be extracted through time warping, yielding the detection model competitive. The validity of the model is verified by hypothetical test. Compared with some state-of-the-art methods, our model achieves an improvement over 5% in the consistency measure of the calf. The symmetry measure of calf and thigh is also test. The proposed method outperforms other methods with the improvement of 12.5%.
Comparing the Predictability of Sensor Modalities to Detect Stress from Wearable Sensor Data

Detecting stress from wearable sensor data enables those struggling with unhealthy stress coping mechanisms to better manage their stress. Previous studies have investigated how mechanisms for detecting stress from sensor data can be optimized, comparing alternative algorithms and approaches to find the best possible outcome. One strategy to make these mechanisms more accessible is to reduce the number of sensors that wearable devices must support. Reducing the number of sensors will enable wearable devices to be a smaller size, require less battery, and last longer, making use of these wearable devices more accessible. To progress towards this more convenient stress detection mechanism, we investigate how learning algorithms perform on singular modalities and compare the outcome with results from multiple modalities. We found that singular modalities performed comparably or better than combined modalities on two stress-detection datasets, suggesting that there is promise for detecting stress with fewer sensor requirements. From the four modalities we tested, acceleration, blood volume pulse, and electrodermal activity, we saw acceleration and electrodermal activity to stand out in a few cases, but all modalities showed potential. Our results are acquired from testing with random holdout and leave-one-subject-out validation, using several machine learning techniques. Our results can inspire work on optimizing stress detection with singular modalities to make the benefits of these detection mechanisms more convenient.
On Frame Fingerprinting and Controller Area Networks Security in Connected Vehicles

Modern connected vehicles are equipped with a large number of sensors, which enable a wide range of services that can improve overall traffic safety and efficiency. However, remote access to connected vehicles also introduces new security issues affecting both inter and intra-vehicle communications. In fact, existing intra-vehicle communication systems, such as Controller Area Network (CAN), lack security features, such as encryption and secure authentication for Electronic Control Units (ECUs). Instead, Original Equipment Manufacturers (OEMs) seek security through obscurity by keeping secret the proprietary format with which they encode the information. Recently, it has been shown that the reuse of CAN frame IDs can be exploited to perform CAN bus reverse engineering without physical access to the vehicle, thus raising further security concerns in a connected environment. This work investigates whether anonymizing the frames of each newly released vehicle is sufficient to prevent CAN bus reverse engineering based on frame ID matching. The results show that, by adopting Machine Learning techniques, anonymized CAN frames can still be fingerprinted and identified in an unknown vehicle with an accuracy of up to 80 %.
On Center Frequency of Gabor Filter for PDP-mesh Inspection

This paper studies center frequency of the Gabor filter for inspection of a mesh fabric for the Plasma Display Panel (PDP-mesh). With a simple defect model simulated shift of yarn density, the relation between the Gabor filter output and the center frequency is theoretically derived. When a ratio of amplitude of the filter output is used to visualize the defect, the results do not depend on the shape of texture in the period and they depend only on the center frequency. Simulations for actual PDP-mesh images showed that the center frequency should be set to half of the texture frequency for the visualization.
Adaptive Vortex Seeking Formation Flight Neurocontrol

This paper focuses on adaptive output-tracking flight control design problem with on-line extremum seeking command generation. The problem is motivated by the need to design an autopilot for autonomous close-coupled formation flight of two aircraft. Control design task is associated with the trailing aircraft only. Flying in formation, the trailing aircraft must constantly seek an optimal relative to the leader position that minimizes aerodynamic drag force induced by the wing tip vortices of the lead aircraft. The trailing aircraft dynamics are represented by two interconnected uncertain subsystems: 1) longitudinal (relative position, true airspeed) dynamics and 2) lateral- directional (lateral position, bank angle, roll rate) dynamics. The two subsystems approximate the trailing aircraft motion in a closed-coupled formation with the leader. While the control command for the first subsystem is predetermined (desired longitudinal separation), the command for the second subsystem (desired lateral separation) is computed on-line such that the influence of the second subsystem on the first one is minimized. Using feedforward neural networks, direct adaptive model reference control, and on-line extremum seeking command generation, the proposed formation flight autopilot provides the trailing aircraft with bounded output tracking and minimizes effects of vortex uncertainty on the aircraft aerodynamic drag force. Boundedness of the tracking error signals in the closed-loop system is shown using Lyapunov direct method.
Life Cycle Assessment of Lithium-ion Battery Pack: Implications of Second-life and Changes in Charging Electricity

The impact of battery electric vehicles (BEV) on global warming is influenced by their battery size and charging electricity source. Therefore, Life Cycle Assessment (LCA) studies of BEV may consider changes in the energy sources for charging and the end-of-life management of used batteries. This study conducts an LCA of a BEV battery pack considering the influences of the charging electricity mix and repurposing the used battery. A cradle-to-grave system is considered to assess the environmental impacts of a Lithium-ion battery (LIB) weighing 290 kg and a pack energy density of 188.3 Wh/kg. The LIB cells were repurposed at their first end-of-life, considering a 50% cell conversion rate (CCR) for 5 years second-life. LCA results show a 6% reduction in GWP impact when the share of renewable sources in the charging electricity mix is considered. Considering recycling, an 11% reduction in GWP is found and less than 1% reduction for repurposing the used LIB cells. The sensitivity analysis found marginal benefits for a longer second-life and higher CCR values (>50%). The parameter with the most influence in the results is the source of electricity to charge the repurposed LIB. Thus, the potential benefits of second-life batteries strongly depend on the source of charging electricity followed by a longer second lifetime (>5 years).
Using CFN for Uniform Sampling of Cloud-Native Datacenters

Uniform sampling has been recently used to detect network anomalies and attacks in an efficient and accurate manner. However, with today’s increasing adoption of large-scale cloud-native datacenter networks, achieving a uniform sampling per application is becoming extremely hard. In this poster we present Cyber-CFN: a model and interface for generating a uniform sampling which is scalable and agnostic to the network architecture. The presented model is based on IETF-LISP and IPFIX standards and the concept of Compute-First Networking (CFN).
Kinestatic Modeling of a Spatial Screw-Driven Continuum Robot

Continuum robots offer many advantages for use in miniature diagnostic and interventional surgical devices. However, the creation of snake-like devices with extremely high slenderness ratios, those with great length and small diameter, remains challenging from a device design perspective. To facilitate improved slenderness ratios, high total accumulated bending angles, and high stiffness and mechanical stability of the active section, we propose the use of a flexible screw-driven mechanism to generate large distal actuation forces while still locating motors and other bulky system components at the base of the device. In comparison to tendon-based designs and push-pull rods, the design avoids the capstan-like buildup of friction. In this work, we present design, fabrication, kinestatic modeling, and experimental validation for a two-degree-of freedom, screw-based, multi-backbone continuum robot. The model that compensates the friction in the system most accurately predicts the behavior of the robot and eliminates most of the hysteresis in the input-output behavior.
“X-Phish: Days of Future Past”‡: Adaptive & Privacy Preserving Phishing Detection

Website phishing continues to persist as one of the most important security threats of the modern Internet era. A major concern has been that machine learning based approaches, which have been the cornerstones of deployed phishing detection solutions, have not been able to adapt to the evolving nature of the phishing attacks. To create updated machine learning models, the collection of a sufficient corpus of real-time phishing data has always been a challenging problem as most phishing websites are short-lived. In this work, for the first time, we address these important concerns and describe an adaptive phishing detection solution that is able to adapt to changes in phishing attacks. Our solution has two major contributions. First, our solution allows for multiple organizations to collaborate in a privacy preserving manner and generate a robust machine learning model for phishing detection. Second, our solution is designed to be flexible in order to adapt to the novel phishing features introduced by attackers. Our solution not only allows for incorporating novel features into the existing machine learning model, but also can help, to a certain extent, the “unlearning” of existing features that have become obsolete in current phishing attacks. We evaluated our approach on a large real-world data collected over a period of six months. Our results achieve a high true positive rate of 97 %, which is on par with existing state-of-the art centralized solutions. Importantly, our results demonstrate that, a machine learning model can incorporate new features while selectively “unlearning” the older obsolete features.
Anticipatory grief prevalence among caregivers of persons with a life-threatening illness: A meta-analysis

Objectives Caring for a family member with a life-threatening illness may lead to increased levels of psychological morbidity. Given the lack of recognition of caregivers’ grief, this study aimed to determine the prevalence of anticipatory grief disorders in caregivers of persons with a life-threatening illness for better intervention and management. Methods CINAHL, Cochrane, Embase, Medline, PubMed, Scopus, PsycINFO and Web of Science were searched up to 21 March 2021 without language and time restrictions. The quality of the included studies was assessed with Hoy’s criteria. A random-effects model was applied to calculate pooled prevalence rates, and multi-regression was performed to examine heterogeneity among studies. Results A total of 3278 citations were retrieved, and 18 studies met the eligibility criteria involving 5470 caregivers. The pooled prevalence of anticipatory grief was 24.78% (95% CI 19.04% to 30.99%). The prevalence rates were significantly higher in female caregivers (16.64%; 95% CI 12.24% to 21.53%) compared with male caregivers (6.11%; 95% CI 4.55% to 7.87%). The married group also had a higher risk (14.66%; 95% CI 10.66% to 19.16%) than single group (5.47%; 95% CI 4.31% to 6.76%). Conclusions The overall pooled prevalence is substantially higher compared with after-loss grief in the general population and supported the presumptions that preloss grief has a greater magnitude compared with after-loss grief. Bereavement support, educational programmes and relevant resources should be delivered even before the actual loss to address the burden of caregivers.
Interprofessional evidence-based counselling programme for complementary and integrative healthcare in patients with cancer: study protocol for the controlled implementation study CCC-Integrativ

Introduction According to international literature, patients with cancer wish to have information on complementary and integrative healthcare (CIH). Medical guidelines recommend actively approaching patients with cancer discussing potential benefits and risks of individual CIH methods. While some CIH methods, for example, acupuncture and yoga, have been proven effective in high-quality studies, other CIH methods lack studies or bear the risk of interactions with chemotherapeutics, for example, herbal drugs. Therefore, an evidence-based interprofessional counselling programme on CIH will be implemented at four Comprehensive Cancer Centres in the federal state of Baden-Wuerttemberg, Germany. Methods and analysis A complex intervention consisting of elements on patient, provider and system levels will be developed and evaluated within a multilayer evaluation design with confirmatory evaluation on patient level. Patients with a cancer diagnosis within the last 6 months will receive three individual counselling sessions on CIH within 3 months (=intervention on patient level). The counselling will be provided by an interprofessional team of medical and nursing staff. For this purpose, an intensive online training programme, a CIH knowledge database and an interprofessional team-building process were developed and implemented (=intervention on provider level). Moreover, training events on the basics of CIH are offered in the outpatient setting (=intervention on system level). Primary outcome of the evaluation at the patient level is patient activation measured (PAM) with the PAM-13 after 3 months. Secondary outcomes, for example, quality of life, self-efficacy and clinical parameters, will be assessed at baseline, after 3 months and at 6 months follow-up. The intervention group (n=1000) will be compared with a control group (n=500, treatment as usual, no CIH counselling. The outcomes and follow-up times in the control group are the same as in the intervention group. Moreover, the use of health services will be analysed in both groups using routine data. A qualitative-quantitative process evaluation as well as a health economic evaluation will identify relevant barriers and enabling factors for later roll-out. Ethics and dissemination The study has been approved by the appropriate Institutional Ethical Committee of the University of Tuebingen, No. 658/2019BO1. The results of these studies will be disseminated to academic audiences and in the community. Trial registration number DRKS00021779; Pre-results.