Miracle-dz/UniFAR-FTUs huggingface dataset:
Real time Application of Malware Patching on Decentralized IoT Systems Through Disease Spread Analysis

IoT networks continue to grow as devices become more accessible and affordable. Due to the mass exodus of production and use, there is room for hackers to take advantage of vulnerabilities in the system. The cloud-based infrastructure although creating an advantage in terms of growing the resources for any given IoT network, the disadvantage in terms of security intrusion are many. In this paper, we analyze a proposed hierarchical distance bound security model. This model takes on a more efficient approach to patching large IoT systems to mitigate the spread of several different types of malware attacks. We utilize SEIR disease spread model to measure the effectiveness of the security model's mitigation of malware spread in the IoT network.
Pattern Analysis of Topological Attacks in Cyber-Physical Power Systems Considering Cascading Outages

Modern cyber-physical power systems are vulnerable to cyber attacks. Given that cyber and physical networks are coupled tightly, attacks in the cyber layer can penetrate the physical layer, causing the outage of transmission lines and other physical equipment, thus changing the topology of the grid. In some extreme scenarios, the topological change will disrupt the emergency response of the grid, eventually causing cascading outages along with a blackout. Therefore, as the defender, the operator of a cyber-physical power system should identify critical cyber attacks. In this paper, patterns of sequential cyber topological attacks are analyzed. Firstly, a coordinated attack process is established, including mechanism and probability analysis considering the different timescales. Secondly, the concept of patterns is defined as minimal attack sequences aimed at causing blackouts. Furthermore, the representativeness of patterns is illustrated, which can significantly reduce the storage of risky attack sequences. Thirdly, to address the problem that the identification of patterns is computationally intensive, a search strategy that selects the next attack target dynamically and increases the search depth gradually is proposed to avoid unnecessary search trials. Lastly, tests are carried out on the IEEE 39-node system using the AC power flow model, which validates the representativeness of patterns and the performance of the proposed search strategy.
Finite Horizon Robust Synthesis

This paper presents a robust synthesis algorithm for uncertain linear time-varying (LTV) systems on finite horizons. The uncertain system is described as an interconnection of a known LTV system and a perturbation. The input-output behavior of the perturbation is described by time-domain Integral Quadratic Constraints (IQCs). The objective is to synthesize a controller to minimize the worst-case performance. This leads to a nonconvex optimization. The proposed approach alternates between an LTV synthesis step and an IQC analysis step. This is analogous to the existing D-K iteration method. Both L2 and terminal Euclidean norm penalties on output are considered for finite horizon performance. A simple example is provided to demonstrate the proposed algorithm.
Constrained and Sparse Switching Times Optimization via Augmented Lagrangian Proximal Methods

In this paper we reformulate a switching times optimization problem with non-uniform switching costs and dwell-time constraints via direct multiple shooting, sparsity-inducing regularization and semi-continuous variables. The transformed problem has composite smooth/nonsmooth objective function and smooth constraints. Necessary optimality conditions for such problems are derived, resembling results from smooth optimization. A safeguarded, primal-dual, augmented Lagrangian proximal method is proposed for its numerical solution, and the global convergence toward points satisfying the necessary conditions is detailed. Finally, numerical results demonstrate the efficacy and limitations of the method.
Robust Max-Min Fairness Energy Efficiency in NOMA-based Heterogeneous Networks

Fairness among different users and system robustness are key issues in the future communication network design. A robust max-min fairness energy efficiency (EE) maximization problem in a downlink non-orthogonal multiple access (NOMA) heterogeneous network is studied when channel state information and interference power are uncertain. A worst-case EE of the small cell is maximized by jointly optimizing the transmit power and cell association under the bounded channel uncertainty model, subject to constraints on the cross-tier interference power, maximum transmit power, and the minimum rate requirement of each small-cell user. The formulated robust max-min fairness EE problem is a mixed-integer and non-convex programming problem with infinite inequality constraints. An iterative resource allocation algorithm is designed based on the proposed power allocation and cell association scheme. Simulation results demonstrate the effectiveness of the proposed robust resource allocation scheme and its improvement over existing schemes.
DeepMal: A CNN-LSTM Model for Malware Detection Based on Dynamic Semantic Behaviours

Malware refers to any software accessing or being installed in a system without the authorisation of administrators. Various malware has been widely used for cyber-criminals to accomplish their evil intentions and goals. To combat the increasing amount and reduce the threat of malicious programs, a novel deep learning framework, which uses NLP techniques for reference, combines CNN and LSTM neurones to capture the locally spatial correlations and learn from sequential longterm dependency is proposed. Hence, high-level abstractions and representations are automatically extracted for the malware classification task. The classification accuracy improves from 0.81 (best one by Random Forest) to approximately 1.0.
Delay-Aware Multi-Source Multicast Resource optimization in NFV-Enabled Network

Network Function Virtualization (NFV) is a transformation of traditional proprietary network designs to a more agile and software based environment. NFV architecture is considered as a key enabler for 5G as it offers the flexible deployment, reduced setup costs and less-time-to-market for the new services. Current studies on NFV in unicast transmission case can not be extended to multicast. Owing to the recent popularity and growing interest for live video streaming applications, efficient multicast solutions in NFV-enabled networks are needed. In this paper, we propose an NFV multicast resource optimization model as a Mixed Integer Linear Program (MILP) exploiting the use of multiple sources and considering the end-to-end delay and bandwidth requirements along with two heuristics algorithms. We evaluate the performance of the proposed algorithms on different network topologies. Simulation results prove that the proposed algorithms outperform the existing solution in terms of reduced bandwidth consumption and the delay values.
Pinocchio: A Blockchain-Based Algorithm for Sensor Fault Tolerance in Low Trust Environment

As the advancement of the IoT market and technology, sensor-equipped devices belonging to different interest groups have more opportunities to cooperate, fulfilling an assignment. How to ensure an agreement on correct sensed data in such a low trust environment is a new challenge in nowadays market. To bridge the gap, we propose Pinocchio, a blockchain-based algorithm which can tolerate faults in WSN (Wireless Sensor Network) along with the ability to defend sensed data against malicious attacks of data tampering and masquerade. Compared to other distributed approaches of sensor fault tolerance, Pinocchio greatly reduced the message complexity of the entire network to $O(N)$ and that of a single node to $O(1)$. Considering the possible waste of resources brought about by vicious competition of hash power in the blockchain-based approach, we design the Geppetto algorithm to supervise and control hash power in a distributed manner, and its effectiveness is demonstrated by experiments.
Driving Envelope Definition and Envelope Protection Using Model Predictive Control

Drive-by-wire technology opens a possibility to help the driver to drive a vehicle safely with support made by electronic control units. This paper presents an approach for defining a Driving Envelope that excludes any not welldefined vehicle states both for longitudinal and lateral dynamics. The second contribution is the design of a Model Predictive Controller for envelope protection that prevents critical situations such as the spinning of the vehicle, blocking of a wheel, and loss of the wheel traction. Validation results demonstrating the performance of the approach are obtained from a fixedsimulator with implemented high-fidelity twin-track model.
Understanding the Performance of Elementary NLA Kernels in FPGAs

Our work compares the use of multi-core hardware and FPGAs to address Numerical Linear Algebra (NLA) kernels. Specifically, we study the behavior of highly tuned kernels in a multi-core CPU processor and OpenCL implementations on FPGAs. For this purpose, we select two of the most significant kernels in the NLA field, the general matrix multiplication (GEMM) and the sparse matrix-vector multiplication (SPMV), which are the most characteristic kernels of dense and sparse NLA respectively. Finally, we perform the experimental evaluation of our implementations on a low-end FPGA platform, and state-of-the-art implementations for traditional CPUs, such as those included in the Intel MKL library, both in terms of runtime and energy consumption.
RouteStitch: Control Traffic Minimization in SDN by Stitching Routes

Software Defined Networking (SDN) is beneficial to many applications, such as intra-datacenter communication, inter-datacenter transportation, etc., due to its centralized control. However, this centralized control frequently makes the controller a bottleneck, due to the large amount of interactions between the controller and switches. In this paper, we characterize such interactions as control traffic, and propose RouteStitch to minimize such kind of traffic. RouteStitch exploits existing route entries in switches to build new paths. To this end, RouteStitch first builds a graph model to describe existing route entries. Then, on such a model, a novel minimum color-alternation routing problem is defined to minimize control traffic, after which an optimal algorithm is proposed on a fixed routing path. For general paths, an $O(\log_{2}L)$-competitive online algorithm is designed to build new paths in an online manner that preserves fundamental property of switch Ternary Content Addressable Memory (TCAM) capacity and allowed maximum hop length L. Extensive simulation results based on realistic topology show that RouteStitch has good performance in terms of reducing control traffic, by 40%.
Defending Link Flooding Attacks under Incomplete Information: A Bayesian Game Approach

The link flooding attack (LFA) arises as a new class of Distributed Denial of Service (DDoS) attacks in recent years. By aggregating low-rate protocol-conforming traffic to congest selected links, LFAs can degrade the connectivity of target servers indirectly. Due to the fast proliferation of insecure Internet of Things (IoT) devices, the deployment of botnets is getting easier, which dramatically increases the risk of LFAs. Since the attacking traffic may not reach the victims directly and seems to be legitimate, LFAs are extremely difficult to detect and defend using traditional methods. In this work, we model the interaction between the LFA attacker and the defender as an extensive form game with incomplete information. By using action space compression and the divide and conquer method, we analyze the Nash equilibrium of the subgame on each link, which reveals the rational behaviors of attackers and the optimal strategies of defenders. Furthermore, we concretely expound how to adopt local optimal strategies in the Internet-wide scenario. Experimental results show the effectiveness and robustness of our proposed decision-making method in explicit LFA defending scenarios.
Three-dimensional Access Point Assignment in Hybrid VLC, mmWave and WiFi Wireless Access Networks

To improve data speed and reliability, hybrid wireless networks combine two different Radio Access Technologies (RATs), such as Visible Light Communications (VLC), millimetre wave (mmWave), Wireless Fidelity (WiFi), 4G Long Term Evolution (LTE), etc. The Internet of Radio Light (IoRL) is a cutting-edge system paradigm to combine three RATs for taking advantage the vast VLC and mmWave spectrum with the ubiquitous coverage of WiFi. In this respect, this work introduces a new convex optimisation-based solution method to optimise the three-dimensional (3D) Access Point Assignment (APA) problem of the IoRL system under individual user positioning, priority and minimum Quality-of-Service (QoS) constraints. We use both the IoRL real-world testbed and large-scale Maltab simulations to evaluate that our solution converges in linear time, and attains higher throughput-vs-fairness trade-off than existing efforts.
FEHash: Full Entropy Hash for Face Template Protection

In this paper, we present a hashing function for the application of face template protection, which improves the correctness of existing algorithms while maintaining the security simultaneously. The novel architecture constructed based on four components: a self-defined concept called padding people, Random Fourier Features, Support Vector Machine, and Locality Sensitive Hashing. The proposed method is trained, with one-shot and multi-shot enrollment, to encode the user’s biometric data to a predefined output with high probability. The predefined hashing output is cryptographically hashed and stored as a secure face template. Predesigning outputs ensures the strict requirements of biometric cryptosystems, namely, randomness and unlinkability. We prove that our method reaches the REQ-WBP (Weak Biometric Privacy) security level, which implies irreversibility. The efficacy of our approach is evaluated on the widely used CMU-PIE, FEI, andFERET databases; our matching performances achieve 100% genuine acceptance rate at 0% false acceptance rate for all three databases and enrollment types. To our knowledge, our matching results outperform most of state-of-the-art results.
Participatory Interventions for Sexual Health Promotion for Adolescents and Young Adults on the Internet: Systematic Review

Background The World Health Organization recommends the development of participatory sexuality education. In health promotion, web-based participatory interventions have great potential in view of the internet’s popularity among young people. Objective The aim of this review is to describe existing published studies on online participatory intervention methods used to promote the sexual health of adolescents and young adults. Methods We conducted a systematic review based on international scientific and grey literature. We used the PubMed search engine and Aurore database for the search. Articles were included if they reported studies on participatory intervention, included the theme of sexual health, were conducted on the internet (website, social media, online gaming system), targeted populations aged between 10 and 24 years, and had design, implementation, and evaluation methods available. We analyzed the intervention content, study implementation, and evaluation methods for all selected articles. Results A total of 60 articles were included, which described 37 interventions; several articles were published about the same intervention. Process results were published in many articles (n=40), in contrast to effectiveness results (n=23). Many of the 37 interventions were developed on websites (n=20). The second most used medium is online social networks (n=13), with Facebook dominating this group (n=8). Online peer interaction is the most common participatory component promoted by interventions (n=23), followed by interaction with a professional (n=16). Another participatory component is game-type activity (n=10). Videos were broadcast for more than half of the interventions (n=20). In total, 43% (n=16) of the interventions were based on a theoretical model, with many using the Information-Motivation-Behavioral Skills model (n=7). Less than half of the interventions have been evaluated for effectiveness (n=17), while one-third (n=12) reported plans to do so and one-fifth (n=8) did not indicate any plan for effectiveness evaluation. The randomized controlled trial is the most widely used study design (n=16). Among the outcomes (evaluated or planned for evaluation), sexual behaviors are the most evaluated (n=14), followed by condom use (n=11), and sexual health knowledge (n=8). Conclusions Participatory online interventions for young people’s sexual health have shown their feasibility, practical interest, and attractiveness, but their effectiveness has not yet been sufficiently evaluated. Online peer interaction, the major participatory component, is not sufficiently conceptualized and defined as a determinant of change or theoretical model component. One potential development would be to build a conceptual model integrating online peer interaction and support as a component.
A Reputation framework with Multiple-threshold Energy Detection in Wireless Cooperative Systems

In this paper, we investigate how to select a trust-worthy Helper as a friendly jammer in a wireless cooperative system (WCS). The selected Helper sends out artificial noise to interfere with an eavesdropper. To ensure that the selected Helper is trustworthy, we design a Dirichlet reputation-based framework and adopt the reputation score to evaluate the trustworthiness of a Helper. To calculate the reputation scores, we develop an artificial noise detection method based on the energy detection with multiple thresholds. According to the multiple-threshold energy detection method, we provide ratings with multiple graded levels (e.g., good-average-bad). In the Dirichlet reputation-based framework, the graded ratings are directly expressed and reflected in the derived reputation scores. Firstly, the a posteriori reputation score is computed by combining the a priori reputation score with the new ratings. Next, a point value is assigned to each rating and the normalized reputation score is computed. Finally, we adopt the normalized reputation score to select a trustworthy Helper as a friendly jammer. Numerical results are presented to demonstrate the performance of our proposed Dirichlet reputation-based framework.
MRGPRX2 and Immediate Drug Hypersensitivity: Insights from Cultured Human Mast Cells.

BACKGROUND AND OBJECTIVE Mast cell (MC) degranulation via activation of the Mas-related G protein-coupled receptor X2 (MRGPRX2) is held to be of key importance for immediate drug hypersensitivity (IDH). However, data in humans are limited to observations in specific cell lines. Objective: To study the usefulness of silencing MRGPRX2 in human MCsto further unveil the MRGPRX2 pathway in IDH. METHODS MCs were cultured out of CD34+ve progenitor cells obtained from peripheral blood (PBCMCs) and incubated with substance P as positive control, rocuronium, moxifloxacin, morphine oramoxicillin. Immunophenotyping of the cells included flow cytometric and microscopic analyses of the expression of CD117, CD203c and MRGPRX2. Intracellular calcium was measured using Fluo-4. Degranulation was analyzed by quantification of CD63expression. For MRGPRX2 silencing, MCs were electroporated with Dicer-substrate silencing RNAs. RESULTS Incubation of MCs with substance P, morphine and moxifloxacin increased intracellular calcium levels and triggered MC degranulation, which, for the drugs, is almost completely abolished by selective MRGPRX2 silencing. Despite an increase in intracellular calcium in MRGPRX2+ve cells, incubation with non-toxic concentrations of rocuronium does not result in degranulation of PBCMCs, whereas amoxicillin has no effect on PBCMCs. CONCLUSION The use of MRGPRX2 silencing inhuman MCs can provide important insights into the role of MRGPRX2 in the pathogenesis of IDH.As induction of calcium signals does not necessarily translate into a secretory response, measurement of the degranulation reaction seems more meaningful in the context of drug testing.
Real-time health monitoring system based on wearable devices

This paper proposed a novel electrocardiogram (ECG) automatic diagnose system for health assistance and rescue related with cardiovascular diseases. This system consists of three parts: 1) Data acquisition subsystem, this subsystem acquires ECG data from wearable devices on users' body and transmit them to the cloud server. 2) Deep learning analysis subsystem, with the help of convolutional neural network, the important feature lied inside ECG signal can be extract for abnormal heart condition detection. Hierarchical residual modules provide the network the ability to see seconds of signal and make a decision through the combination of features. Meanwhile, the global max pooling layer on top of the network enables it to capture the most important feature across the whole ECG signal with periodicity. This subsystem is a crucial part for cardiac status based health caring. 3) Back-stage management subsystem, methodical data storage and management were conducted in this subsystem, which also provides the users an interface to access their healthy data and body status. Assembling these three parts of system, real-time ECG diagnose for people in need and timely medical rescue can be implemented.
Traceable Revocable Anonymous Registration Scheme with Zero-knowledge Proof on Blockchain

User registration is the beginning of the life cycle of an account for most information systems. Some registration servers have special requirements for the validity of the registrant’s identity and attributes, and thus demand his real-life identity and raw attributes for verification, which poses a great threat to privacy. However, the exposure of the registrant’s real-life identity and raw attributes is not necessary, as long as he can prove to the server that he is associated with a real-life identity (RId) that fulfills the requirements. In this paper, we propose the concept of anonymous registration, with which a user can register an anonymous identity (AId) without privacy leakage. Anonymous registration has two basic features, i.e. privacy and verifiability, and two extended features, i.e. traceability and revocability. We design and implement an anonymous registration scheme with the above features, named Traceable Revocable Anonymous Registration Scheme (TRARS) based on Blockchain. In the proposed registration process, the user can calculate a zero-knowledge proof for a self-generated AId, which can prove to the registration server that the AId is associated with a valid RId and the RId’s attributes meet the server’s requirements, without leaking any privacy. We also introduce an identity revocation coordinator, with which evil identities can be tracked and outdated AIds can be revoked timely.
Study of the graduates’ perspectives on the quality assessment of two engineering programs at a Peruvian university

The importance of knowing the viewpoints of engineering graduates from the University of Science and Humanities (UCH), located in Lima, Peru, is crucial to make continuous improvements in the curricula of its two programs, electronic and systems engineering. Hence, in this study, a survey was carried out to find out the perspectives about the content learned in their classes as well as the characteristics and the quality of the methodology followed in them. In addition to this, they were asked about the quality of the administrative services at the university. The results of our study show a general contentment as to how the programs are being currently handled. However, there are some details that need to be corrected in order to reach an optimal point in which future graduates are completely satisfied with both engineering programs.