Miracle-dz/UniFAR-FTUs huggingface dataset:
Preventive Priority Setting Against Multiple Controller Failures in Software Defined Networks

This paper proposes a preventive priority setting model to minimize the worst-case maximum utilization ratio against multiple controller failures in software defined networks. For a set of controllers able to manage a switch, we introduce a priority for each controller to become the main controller that controls the switch. Once the existing main controller fails, the survived controller which has the highest priority works as the main controller. This reassignment is automatically obtained according to the priority setting decided at the network operation start time. In this way, the proposed model provides a prompt recovery with reducing the network instability due to unnecessary controller reassignment. We formulate the proposed model in two different forms, which are an integer linear programming formulation and a min-max formulation. We prove that the considered problem is NP-hard. A basic heuristic is introduced based on the min-max formulation. Its two extensions are further developed considering the accuracy and the computation time in practical computation. Numerical results reveal that, compared to two baselines that sacrifice certain network stability to achieve a more flexible reassignment, the proposed model reduces the network instability by 27% and 52% in average, respectively, with obtaining the comparable maximum utilization ratio.
Reifying Dominant Ideologies: Consequences of Decoupling Equity from Ethics in Engineering Education

In this paper, we conduct a critical, secondary analysis of three engineering leadership research projects to explore the consequences of separating ethics from equity in engineering education and practice. Our findings suggest that by pairing ethics with equity, the Canadian Engineering Accreditation Board (CEAB) has raised the profile of professional responsibility among engineering education administrators. Unfortunately, by treating ethics and equity as distinct skillsets rather than integrated epistemological practices, we fail to disrupt powerful ideologies in the profession. In the process, we run the risk of universalizing ethical dilemmas faced by socially advantaged engineers, masking career mobility penalties faced by marginalized members of the profession and leaving engineering leaders with strong social impact records off the hook for inequity. In short, by decoupling equity from professional ethics, we leave societal patterns of privilege intact in workplaces designed for something other than teaching and learning.
Dynamic Network Tomography with Sparse Recovery-based Link Delay Estimation

When the scale of communication networks has been growing rapidly in the past decades, it becomes a critical challenge to extract fast and accurate estimation of key state parameters of network links, e.g., transmission delays and dropped packet rates, because such monitoring operations are usually time-consuming. In view of that fact that prior studies can infer link delays from a limited number of measurements using compressed sensing, we particularly extend to networks with dynamic changes including link insertion and deletion. Moreover, we propose a more efficient algorithm with a better theoretical upper bound. The experimental result also demonstrates that our algorithm outperforms the previous work in running time while maintaining similar recovery performance, which shows its capability to cope with large-scale networks.
On the Effect of Log-Mel Spectrogram Parameter Tuning for Deep Learning-Based Speech Emotion Recognition

Speech emotion recognition (SER) has become a major area of investigation in human-computer interaction. Conventionally, SER is formulated as a classification problem that follows a common methodology: (i) extracting features from speech signals; and (ii) constructing an emotion classifier using extracted features. With the advent of deep learning, however, the former stage is integrated into the latter. That is to say, deep neural networks (DNNs), which are trained using log-Mel spectrograms (LMS) of audio waveforms, extract discriminative features from LMS. A critical issue, and one that is often overlooked, is that this procedure is done without relating the choice of LMS parameters to the performance of the trained DNN classifiers. It is commonplace in SER studies that practitioners assume some “usual” values for these parameters and devote major efforts to training and comparing various DNN architectures. In contrast with this common approach, in this work we choose a single lightweight pre-trained architecture, namely, SqueezeNet, and shift our main effort into tuning LMS parameters. Our empirical results using three publicly available SER datasets show that: (i) parameters of LMS can considerably affect the performance of DNNs; and (ii) by tuning LMS parameters, highly competitive classification performance can be achieved. In particular, treating LMS parameters as hyperparameters and tuning them led to ~23%, ~10%, and ~11% improvement in contrast with the use of “usual” values of LMS parameters in EmoDB, IEMOCAP, and SAVEE datasets, respectively.
A Data-Driven Approach to Predict Hourly Load Profiles From Time-of-Use Electricity Bills

The design of renewable-based and collective energy systems requires consumption data with fine temporal and spatial resolution. Despite the increasing deployment of smart meters, obtaining such data directly from measurements can still be challenging, particularly when attempting to gather historical data over a reasonable period for many end users. As a result, there is a need for models to simulate or predict these consumption data (e.g., hourly load profiles). Typically, these models rely on numerous specific and detailed observations, such as load type, household size for residential customers, or operating hours for commercial ones. However, gathering this level of detail becomes increasingly difficult as the number and diversity of end users increase. Therefore, this paper proposes a data-driven approach to predict hourly load profiles of heterogeneous end users using only their monthly time-of-use electricity bills as inputs. We create a training set using a limited number of hourly measurements from diverse categories of end users and, differently from other approaches aimed at classifying the end users, we develop a regression model to map monthly electricity bills to typical load profiles. Experimental results using one year of data from various end-user categories demonstrate an average normalized mean absolute error of approximately 26% for instantaneous consumption and less than 4% for time-of-use values. Comparative analysis with standard load profiles and a two-step data-driven approach based on classification reveals that our proposed method outperforms the others in terms of prediction accuracy and statistical metrics.
Possibility of an Akhmediev breather decaying into solitons

We consider the process by which a constant or wide-pulse solution of the nonlinear Schr\"odinger equation can become converted into an ensemble of solitons by modulational instability. This process is generally believed to be one important step in the generation of supercontinuum in optical fibers. Starting from the Akhmediev breather solution we study the conversion by two methods: One is pulse shape-oriented and uses the soliton relation between width and peak power. The other is eigenvalue-oriented and uses results from scattering theory. It becomes clear that an evolution according to the unmodified nonlinear Schr\"odinger equation, even in the presence of noise, will not lead to the transformation into solitons. If one takes the Raman effect into account, however, a conversion to a soliton gas takes place.
Extraction of Fetal ECG Signals Based on PCA and Improved FastICA Algorithm

In order to solve the problem that the fast independent component analysis (FastICA) algorithm is sensitive to the initial value and the separated signal contains interference signals, this paper proposes a method combining principal component analysis (PCA) and improved FastICA algorithm, and applies it to extract fetal Electrocardiogram (FECG). Firstly, a number of major sources signals are extracted from the parent abdominal mixed signal using PCA. Secondly, the randomly generated initial values are processed by the steepest descent method, and then the source signals are separated by the double convergence factor FastICA algorithm to drive them into the Newtonian convergence region, and finally, the channel where the FECG is located is determined according to the sample entropy and processed by the wavelet thresholding method. The simulation results show that the algorithm not only alleviates the problem of initial value sensitivity, but also improves the convergence speed, extracts clear FECG signals, and almost does not lose the separation accuracy.
Evaluating the Impact of Optimizations for Dynamic Binary Modification on 64-bit RISC-V

Dynamic Binary Modification (DBM) is an important technique used in computer architecture simulators, virtualization, and program analysis, to name a few examples. The software ecosystem of RISC-V is maturing at pace, but is still missing a high-performance, optimized DBM. Addressing this requirement is key to improving the overall software ecosystem. This paper presents a comprehensive performance evaluation study for a DBM (MAMBO) which has been ported and optimized for 64-bit RISC-V. The main optimizations for DBM on RISC architectures have been implemented and tuned for RISC-V to address specific architectural features. For example, jump trampolines have been specifically developed to address the short direct branch range specified by the RISC-V ISA. The evaluation shows that for SPEC CPU2006 the geometric mean overhead is of 14.5%, with SPECint having the largest contribution with a geometric mean of 28.5%, while SPECfp has only an overhead of 5.6%. Concretely, this results in a reduction in runtime for h264ref from over 75 hours using the baseline DBM, to 2.2 hours with optimizations applied.
The potential for co-operatives to mitigate AI ethics catastrophes: perspectives from media analysis

Would the world have seen less AI-related scandals if more AI companies operated as co-operatives? As a response to multiple high profile tech scandals within the last decade, there has been an increased call for introducing more accountability in the AI industry. However, it is unclear to what degree the proposed efforts have been or will be effective in practice. The question remains whether these incremental, multi-stakeholder AI ethics efforts are in fact trying to address a fundamentally systemic issue inherent to the existing corporate power structure. As an attempt to address this question, we gain an understanding of the major themes in high profile AI-related catastrophes in the last four years (2018–2021) through an inductive media analysis. We then investigate how the principle of democratic gov-ernance and distributive executive power - core to co-operative organization structure - could have prevented or mitigated the contributing factors of the reported events. We find that the vast majority (71%) of the recent AI ethics scandals are not the result of a lack of knowledge or tools, but attributed to power dynamics that hinder the ability of internal stakeholders from taking action. We present the co-operative governance structure as a possible mitigating solution to addressing future AI ethics catastrophes, and provide a critical look at practical challenges inherent to AI co-operatives.
Code Property Graph based Vulnerability Type Identification with Fusion Representation

Deep learning-based vulnerability detection methods have become one of the mainstream methods of vulnerability detection. The vulnerability type information is of great value in helping vulnerability location and vulnerability remediation. This paper proposes a framework for Vulnerability Type Identification based on Code Property Graph with Fusion Representation. First, this paper uses code property graph information. Code property graph(CPG) is a joint data structure that combines Abstract Syntax Trees(AST), Control Flow Graphs (CFG), and Program Dependency Graphs (PDG). We encode CPG information. Secondly, we use Convolutional neural network combined with Recurrent Neural Network(CNN-RNN) and Attention-Based Bidirectional Gate Recurrent Unit (Att-BiGRU) to extract AST and CFG combined with PDG information. We fuse the extracted features to obtain an effective representation. And then, we perform multi-classification to derive the predicted value of the vulnerability type. Finally, we use 59 vulnerabilities with third-level CWE-ID for evaluation. The experiments show that this paper’s code property graph information can better represent the type information of vulnerabilities. Compared with the classical RNNs, our model in this paper has a more accurate identification effect.
Design of Secure and Lightweight Authentication Scheme for UAV-Enabled Intelligent Transportation Systems Using Blockchain and PUF

Unmanned-aerial-vehicle (UAV)-enabled intelligent transportation system (ITS) is an advanced technology that can provide various services including autonomous driving, real-time creation of high-definition maps, and car sharing. In particular, a UAV-enabled ITS can be realized through the combination of traditional vehicular ad hoc networks (VANETs) and UAVs that can act as flying roadside units (RSUs) at the outskirts and monitor road conditions from predefined locations to spot car accidents and any law violations. Notably, to realize these services, real-time communication between UAVs and RSUs must be guaranteed. However, UAVs have limited computing powers, and if extensive computation is required during communication, the provision of real-time ITS services may be hindered. Furthermore, UAVs and RSUs communicate via public channels that are prone to various attacks, such as replay, impersonation, trace, and session key disclosure attacks. Thus, in this article, a secure and lightweight authentication scheme is proposed for UAVs and RSUs using the blockchain technology. The proposed scheme is analyzed using informal and formal methods including Burrows–Abadi–Nikoogadam (BAN) logic, automated validation of internet security protocols and applications (AVISPA) simulation tool, and real–or–random (RoR) model, and its performance is compared with that of related schemes. The results reveal that the proposed scheme is more efficient and secure as compared to the other competing schemes.
Average Task Execution Time Minimization under (m, k) Soft Error Constraint

Safety-critical systems are often subjected to transient faults. Since these transient faults may lead to soft errors that cause catastrophic consequences, error-handling must be addressed by design. Full-protection against faults is too costly in terms of resource usage. A common approach to relax the resource demands and limit the impact of errors is to consider (m, k)-constraints, which requires that at least m jobs out of any k consecutive jobs are error-free. To assure (m, k)-compliance, static patterns are widely used to select the job execution modes, i.e., either in an error-free mode at the cost of increased worst-case execution time or in an error-prone mode with the advantage of less execution time. Although static patterns have been shown to be effective in energy-aware designs, resource over-provision is inevitable due to the relatively low rate of error probability. In this work, we propose two dynamic (and adaptive) approaches that allow the scheduler to opportunistically select execution modes based on the error-history of the past jobs and the actual error probability. We firstly propose a Markov chain based solution if the error-probability is known and static and secondly a reinforcement learning-based approach that can handle unknown error probabilities. Experimental evaluations show that our approaches outperform the state-of-the-art in most of the evaluated cases in terms of average utilization for each task and the overall utilization for multitask systems.
THE ALFALFA Hα SURVEY. I. PROJECT DESCRIPTION AND THE LOCAL STAR FORMATION RATE DENSITY FROM THE FALL SAMPLE

The ALFALFA Hα survey utilizes a large sample of H i-selected galaxies from the ALFALFA survey to study star formation (SF) in the local universe. ALFALFA Hα contains 1555 galaxies with distances between ∼20 and ∼100 Mpc. We have obtained continuum-subtracted narrowband Hα images and broadband R images for each galaxy, creating one of the largest homogeneous sets of Hα images ever assembled. Our procedures were designed to minimize the uncertainties related to the calculation of the local SF rate density (SFRD). The galaxy sample we constructed is as close to volume-limited as possible, is a robust statistical sample, and spans a wide range of galaxy environments. In this paper, we discuss the properties of our Fall sample of 565 galaxies, our procedure for deriving individual galaxy SF rates, and our method for calculating the local SFRD. We present a preliminary value of log(SFRD[M⊙ yr−1 Mpc−3]) = −1.747 ± 0.018 (random) ±0.05 (systematic) based on the 565 galaxies in our Fall sub-sample. Compared to the weighted average of SFRD values around z ≈ 2, our local value indicates a drop in the global SFRD of a factor of 10.2 over that lookback time.
Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels

As the number of satellite networks increases, the radio spectrum is becoming more congested, prompting the need to explore higher frequencies. However, it is more difficult to operate at higher frequencies due to severe impairments caused by varying atmospheric conditions. Hence, radio channel forecasting is crucial for operators to adjust and maintain the link’s quality. This paper presents a practical approach for Q/V-band modeling for low Earth orbit satellite channels based on tools from machine learning and statistical modeling. The developed Q/V-band LEO satellite channel model is composed of: 1) forecasting method using model-based deep learning, intended for real-time operation of satellite terminals; and 2) statistical channel simulator that generates a time-series path-loss random process, intended for system design and research. Both approaches capitalize on real-measurements obtained from AlphaSat’s Q/V-band transmitter at different geographic latitudes. The results show that model-based deep learning can outperform simple statistical and deep learning methods by at least 50%. Moreover, the model is capable of incorporating varying rain and elevation angle profiles.
GHunter: A Fast Subgraph Matching Method for Threat Hunting

Threat hunting is the process of proactively searching for known attack behavior in an organization’s information system. A popular approach to threat hunting uses cyber threat intelligence (CTI) to identify advanced persistent threats (APTs) that are hidden in kernel-level audit logs (e.g., whole-system data provenance). However, existing threat hunting mechanisms can-not produce timely results due to the enormous size of provenance data. As a result, threat hunting cannot help sysadmins to quickly recognize an ongoing APT campaign and immediately block any subsequent attack activity. In this paper, we propose GHunter, a system that performs approximate subgraph matching using graph neural networks (GNNs) to quickly and accurately hunt APTs. GHunter first converts known APT scenarios and provenance logs into graph data. Then, GHunter uses GNNs to embed APT scenario graphs and provenance graphs to discover any subgraph relationships. If an APT scenario graph is a subgraph of a provenance graph, GHunter alerts to sysadmins the presence of the corresponding APT scenario in the system. We use DARPA’s Transparent Computing (TC) datasets to evaluate GHunter’s performance. The results show that GHunter achieves 97% accuracy when hunting APTs from millions of provenance log entries and spends 195x less execution time than prior work.
Who Gets in the Way of Parallelism? Analysis and Optimization of the Parallel Processing Bottleneck of SDN Flow Rules in ONOS

Software-Defined Networking (SDN) decouples the data plane from the control plane, enabling centralized control and open programmability of the network. OpenFlow flow rules are the key carrier for the SDN application to configure and manage the data plane through the control plane, and the processing efficiency of flow rules of the SDN controller in the control plane is critical as it will directly impact the instantaneity of configuring and managing the data plane. Currently, the controller increases the processing efficiency of flow rules by means of multi-threaded parallel processing. However, in the experiments of the widely used SDN controller ONOS, we found a new bottleneck in the parallel processing of flow rules that causes the performance gains from parallelism to be offset. Therefore, in this paper, we locate the bottleneck and analyze its causes through source code analysis and timestamp tests, propose a parallel event queue to resolve the bottleneck, and implement it in ONOS. Experiments show that our improved ONOS effectively resolves the bottleneck problem and achieves an average 3.57x improvement in the processing efficiency of flow rules compared to the original ONOS.
Jamming Gripper-Inspired Soft Jig for Perceptive Parts Fixing

To achieve precise and time-efficient operation to ensure stable and reliable product assembly, custom-made jigs have been developed for each assembly part. However, designing these jigs is a time-consuming task. Therefore, this study proposes a jamming gripper-inspired flexible jig using a soft membrane consisting of transparent beads and oil with an adjusted refractive index. The proposed jig is equipped with a hydraulic drive system with visual control of the amount of transparent oil inside the jig. The proposed system enables parts fixing by creating a jammed state while maintaining optical transparency, thereby facilitating the visual sensing of the jig’s membrane from camera sensors embedded in the jig. Furthermore, a sensing method was developed to perform a precise assembly without external sensors to estimate the pose of an object based on the proposed point-to-function iterative closest point. The results show that the proposed system successfully estimates the orientation at a root mean square error of less than ang 4 when the position is varied from <inline-formula> <tex-math notation="LaTeX">$\mathrm {0 mm}$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$\mathrm {20 mm}$ </tex-math></inline-formula> and the angle is varied from ang 0 to ang 20. Furthermore, it can repeatedly fix the object in any orientation from ang 0 to ang 20 at a position of less than <inline-formula> <tex-math notation="LaTeX">$\mathrm {0.5 mm}$ </tex-math></inline-formula> and an angle of less than ang 1.1.
A Logical Method to Predict Outcomes After Coronary Artery Bypass Grafting

This paper analyzes data from coronary artery bypass grafting (CABG) using decision functions to represent rules. The data was collected at the University Hospital in Umeå, Sweden. The data contains pre-, intra-, and postoperative detail from 2975 heart operations during 1993-96. Each instance is represented by 14 preoperative variables, 4 intraoperative variables, and 9 postoperative variables. A logical method is used to predict the postoperative variables using preoperative variables. First, each postoperative variable is represented as a decision functions of preoperative variables. Then, for each postoperative variable, a minimal set of preoperative variables is derived. And finally, each postoperative variable is represented by a minimum set of rules using preoperative variables. With this method we can predict postoperative outcome, where prediction using preoperative data only is of particular interest e.g. for surgery scheduling.