Miracle-dz/UniFAR-FTUs huggingface dataset:
Protecting Virtual Machines against Untrusted Hypervisor on ARM64 Cloud Platform

In cloud computing, the confidentiality and integrity of virtual machines (VMs) are facing severe threats because of the huge trusted computing base (TCB) software stack in virtualization layer. With the increasing momentum of ARM64 in cloud computing server markets, it is important to protect VMs from privileged software (including host operating system and hypervisor) on ARM64. In this paper, we have created SecureHyp, a new virtualization platform design for refactoring the existing hypervisor using the ARM64 hardware security mechanisms to reduce the TCB while protecting VMs against untrusted privileged software. Based on the principle of the least privilege, SecureHyp separates the sensitive-resource management from the rights of the hypervisor and prohibits the hypervisor from accessing specific sensitive resources. By deploying the memory isolation using ARM Trusted Firmware (ATF) and virtual Memory Management Unit (vMMU), SecureHyp ensures both the security and efficiency of the guest VMs. We have implemented SecureHyp on Linux firefly-4.4. 194 with modest modification. The results show that SecureHyp can protect the confidentiality and integrity of virtual machines with only around 2000 lines of code software TCB and negligible performance overhead.
Axioms and Theorems for Integers, Lists and Finite Sets in Logic for Computable Functions (LCF)

Abstract : LCF (Logic for Computable Functions) is being promoted as a formal language suitable for the discussion of various problems in the Mathematical Theory of Computation (MTC). To this end, several examples of MTC problems have been formalised and proofs have been exhibited using the LCF proof-checker. However, in these examples, there has been a certain amount of ad-hoc-ery in the proofs: namely, many mathematical theorems have been assumed without proof and no axiomatisation of the mathematical domains involved was given. The paper describes a suitable mathematical environment for future LCF experiments and its axiomatic basis. The environment developed, deemed appropriate for such experiments, consists of a large body of theorems from the areas of integer arithmetic, list manipulation and finite set theory.
The Scattering Cross Section for a Target Irradiated by Time-varying Electromagnetic Waves

Based on the analysis of the target cross section, the cross section expression for a target irradiated by the time-varying electromagnetic waves is presented utilizing its cross section in frequency domain. Result shows that the cross section of a target is easy obtained in time domain if only its cross section in frequency domain is known. This cross section is related to the factors of target scale, shape, the electromagnetic parameters of the medium of the target and the time-varying incident waves. This cross section is not restricted by the bandwidth. Finally its expression in engineering usage is presented.
THE TURBULENT CASCADE AND PROTON HEATING IN THE SOLAR WIND AT 1 AU

We examine the convergence of third-order structure function expressions derived to measure the rate of turbulent energy cascade within the solar wind using Advanced Composition Explorer observations from 1 AU over the years 1998 through 2007. We find that a minimum of a year of data is normally required to get good convergence and statistically significant results. We then apply these findings to 10 years of observations spanning both solar minimum and solar maximum conditions. We compare the computed energy cascade rates with previously determined rates of proton heating at 1 AU as determined from the radial gradient of the proton temperature to be proportional to the product of wind speed and proton temperature. We find good agreement with a moderate excess of energy within the cascade that is consistent with previous estimates for thermal electron heating in the solar wind. In keeping with earlier analyses of the dissipation spectrum, we postulate that electron heating by the turbulent cascade is less than and at most equal to the rate of proton heating.
The strong correlations between the performance of a KB supercapacitor and the properties of NaClO4 and LiFSI electrolytes over wide concentration ranges

Aqueous supercapacitors have received significant attention for their high safety, environmental friendliness, and high ionic conductivity. In particular, the supercapacitors with highly concentrated water‐in‐salt electrolytes (WiSEs) exhibit outstanding characteristics such as wide electrochemical stability windows (2‐3 V) and fast ion transport behaviors. However, previous studies on the WiSE supercapacitors have mostly focused on the effects of high concentrations, and few have examined the influence of the electrolytic structure upon the performance of supercapacitors with various concentrations. Hence, the present study examines how the concentration affects the performance of supercapacitors with various concentrations of NaClO4 (1‐17 mol kg−1) and LiFSI (1‐35 mol kg−1) electrolytes. The study reveals a strong correlation between the electrochemical performance and the concentration‐dependent properties of the electrolyte. Sudden increases in the cell voltages, along with the maximum response speeds of the supercapacitors (−Φ120 Hz = 80.5° and 77.9° for NaClO4 and LiFSI, respectively) are observed in the middle of the concentration range (i.e., at 5‐6 mol kg−1), and are strongly associated with a critical change in the ion and water aggregates in the unique biphasic aqueous electrolyte. The present work is of great importance in gaining a fundamental understanding of the aqueous electrolytes with chaotropic ion salts and is expected to contribute greatly to the development of ultrafast supercapacitors based on the highly conductive electrolytes.
Sum Rate Maximization for Multi-IRS Assisted Downlink NOMA with Mobile Users

Intelligent reflection surface (IRS) has been recognized as an emerging revolutionary technology for the next generation wireless communication. In this paper, we consider a novel multi-IRS assisted downlink non-orthogonal multiple access (NOMA) network to enhance the system capacity. The superposed signals are transmitted from the base station (BS) to multiple mobile users via multi-IRS reflection. We formulate the sum rate maximization problem by optimizing reflection coefficients. In order to solve the non-convex optimization problem, we utilize the methods of semidefinite relaxation (SDR) and Gaussian randomization. Furthermore, our simulation results prove that the performance of the proposed multi-IRS-NOMA system with optimal reflection coefficients is significantly enhanced.
Effect of Polyphenolic Complements on Cognitive Function in the Elderly: A Systematic Review

Polyphenols have been shown to be effective against many chronic diseases. These compounds could have a beneficial effect at the cognitive level. The exact mechanism by which they provide positive effects at the cognitive level is not well known, but it is believed that they could counteract neuroinflammation. The objective of this study is to review nutritional interventions that include foods or supplements rich in flavanols, flavonols, or stilbenes to the usual diet on cognitive deterioration in people over 50 years of age. Clinical trials published in PubMed and Web of Science from 1 March 2010 to 1 March 2020 were explored, from which 14 studies were selected. All of them showed some improvement after the intervention. In interventions with flavanols and stilbenes, relevant improvements have been observed both in healthy patients and in patients with established cognitive impairment. Most studies agree that the greatest benefits are found with high doses and longer duration treatments. The changes were fundamentally assessed through cognitive tests, and in some of the studies, through magnetic resonance imaging (fMRI). The type of cognitive test used to assess the effect of the intervention was revealed to be critical. Several studies have also shown improvements in analytical parameters and blood pressure.
Public acceptance of wind energy – concepts, empirical drivers and some open questions

Abstract. The further development of wind energy is of major importance for the success of the energy system transformation in Germany and elsewhere. This transition process is not an easy task. For example, the yearly installed capacity of wind energy onshore in Germany has been declining since 2017. Only relatively few new wind turbines were constructed especially in 2019. Problems are, for example, minimum distance requirements (e.g. residential areas, air safety), the high complexity of planning processes and local protests. Social science research has now dealt with the topic of public wind energy acceptance for quite some time. On the one hand, the specific kind of acceptance (e.g. local acceptance) has been subject to scientific discourse. On the other hand, different empirical drivers (e.g. perceived distributional or procedural fairness, trust in relevant actors of the transformation process, risk–benefit perceptions, participation) have been of special interest. This review deals with central definitions and concepts, as well as qualitative and quantitative empirical findings, of social science research concerning the acceptance of wind energy in Germany and elsewhere. Although there has been already a lot of valuable scientific work done, there are still some open questions left.
Learning-Based Task Offloading for Mobile Edge Computing

Mobile edge computing (MEC) is an important technology for latency-sensitive applications. One of the biggest challenges in MEC is efficiently allocating resources under strict QoS requirements and resource constraints. The purpose of this paper is to study the joint problem of computation offloading and resource allocation in such networks. The problem is formulated as a mixed-integer non-convex optimization problem and is proved to be NP-hard. In order to solve it efficiently, we propose a multi-agent deep reinforcement learning solution based on actor-critic method. To reduce system latency, each agent aims to learn interactively the best offloading policy independently of other agents. The simulation results illustrate the performance and advantages of the proposed solution compared to benchmark solutions.
ML-ECN: Multi-Level ECN Marking for Fair Datacenter Traffic Forwarding

Recently, Explicit Congestion Notification (ECN) has been leveraged by most Datacenter Network (DCN) protocols for congestion control to achieve high throughput and low latency. However, the majority of these approaches assume that each switch port has one queue while current industry trends towards having multiple queues per each switch port. To this end, we propose ML-ECN, a Multi-Level probabilistic ECN marking scheme for DCNs enabled with multiple-service, multiple-queue switch ports. The main design of ML-ECN relies on the separation between small, medium, and large flows by dedicating multiple queues for each flow class to ensure fairness enqueueing. ML-ECN employs a single threshold for each queue in small service-queue class and multiple thresholds with a probabilistic marking for each queue in medium and large service-queue classes to achieve low latency for mice (small) and high throughput for elephant (large) flows. In addition, ML-ECN performs fairness-aware ECN marking that ensures small flows never get marked at early queue build up. Large-scale ns-2 simulations show that ML-ECN outperforms existing approaches for different performance metrics.
Severity Assessment of a Reported Bug by Considering its Uncertainty and Irregular State

An accurate bug severity assessment is an important factor in bug fixing. Bugs are reported on the bug tracking system by different users with a fast speed. The size of software repositories is also increasing at an enormous rate. This increased size often has much uncertainty and irregularities. The factors that cause uncertainty are biases, noise and abnormality in data. The authors consider that software bug report phenomena on the bug tracking system keeps an irregular state. Without proper handling of these uncertainties and irregularities, the performance of learning strategies can be significantly reduced. To incorporate and consider these two phenomena, they have used entropy as an attribute to assess bug severity. The authors have predicted the bug severity by using machine learning techniques, namely KNN, J48, RF, RNG, NB, CNN and MLR. They have validated the classifiers using PITS, Eclipse and Mozilla projects. The results show that the proposed entropy-based approaches improves the performance as compared to the state of the art approach considered in this article.
Robust Energy-Efficient Optimization for Heterogeneous Networks with Residual Hardware Impairments

Resource allocation is very important for achieving interference suppression and protecting the quality of service of users in heterogeneous networks (HetNets). However, the existing works with perfect channel state information (CSI) and ideal hardware ignored the impact of channel uncertainties and hardware impairments on system performance. In this paper, we design a robust secure resource allocation algorithm with imperfect CSI to achieve the energy efficiency (EE) maximization of femtocell users for a two-tier downlink HetNet with multiple passive eavesdroppers, where the residual hardware impairments are considered at the transceivers. The formulated EE problem is non-convex with the consideration of the maximum transmit power constraint of each base station, the cross-tier interference power constraint, as well as the secure rate constraint. By using the worst-case approach and successive convex approximation, the resource allocation problem with the infinite-dimensional constraints is converted into a convex one which is efficiently solved by using convex optimization theory. Simulation results verify that the proposed algorithm has a higher EE and causes less interference power to macrocell users by comparing it with the baseline algorithms.
Pro-Poor Growth in Africa’s economies

Pro-poor growth is a term used for primarily national policies to stimulate economic growth for the benefit of poor people (primarily in the economic sense of poverty). This notion has been observed largely in the developing countries, particularly in Africa. There are challenges that have been observed and various shifts in the economic development, but the growth is gradual, this is due to various factors that fail and still fail to be addressed by many policy makers. One of these factors includes regressive dominant paradigms that have limited our understanding of the development challenges, particularly of Africa and developing countries. The paper therefore gives a brief critique of traditional concepts of growth and builds a case for broadening these concepts, particularly in as far as these relate to the pursuit of meaningful economic growth in Africa. It argues that these misconceptions or inability to conceptualise the multiple dimensions of growth hamper our ability as scientists and policymakers to adapt to the changes or sustainable dynamics of economic revamping. The impact on economic growth has a lot of challenges which still remain unaddressed. There is still a gap that remains unclosed.
A Lightweight Semi-Supervised Learning Method Based on Consistency Regularization for Intrusion Detection

With the development of the Industrial Internet of Things (IIoT), more frequent attacks occur to intrude IIoT devices. A reasonably designed intrusion detection method can effectively guarantee the security of IIoT. Over the past decade, different methods of intrusion detection based on deep learning (DL) have been proposed, which helps intrusion detection keep evolving and become more robust. However, these previous researches usually require the participation of a large number of experts, and gradually become invalid with the continuous development of intrusion methods. The limited compute capability of IIoT devices also greatly hinder the deployment of overly complex DL models. To address these challenges, this paper proposes a lightweight semi-supervised learning (LSSL) method based on consistency regularization for intrusion detection. Our proposed method enhances the detection performance by using unlabeled traffic data for consistency training. Besides, we adopt separable convolutions for efficient feature extraction. Experimental results on two widely-used benchmark datasets show that the detection performance of our model is significantly improved by the consistency training, and it can effectively detect various attacks in complex networks.
Self-Heating Aware Design of ICs in Deep Sub-Micron FDSOI and Bulk Technologies

Bulk CMOS technologies left the semiconductor market to the novel device geometries such as FDSOI and FinFET below 30 nm, mainly due to their insufficient electrical characteristics arising from different physical limitations. These innovative solutions enabled the ongoing device scaling to continue. However, the threshold voltage and the power supply values did not shrink with the device sizes, which caused an excessive amount of heat generation in very small dimensions. With the high thermal resistivity materials used in FDSOI and FinFET, the generated heat cannot leave the device easily, which is not the case in bulk. With all of these, modern geometries brought a major problem, which is the self-heating. Due to self-heating effects (SHE), the temperature of a device rises significantly compared to its surroundings. Having very large local temperature brings important reliability issues. Moreover, the electrical behaviour of a device also changes dramatically when its temperature is very large. These facts bring the need of considering SHE and the temperature of each device separately. Nevertheless, in many of today’s CAD tools, a single global temperature is applied to all of the devices. Even if some advanced simulation options are used, estimating the temperature of a device is not a simple task as it depends on many parameters. The focus of this thesis is to show the significance of SHE in the design of ICs and provide selfheating aware design guidelines. In order to achieve this, different circuit implementations are studied by considering the SHE. The study consists of two main parts, which are the reliability of the high-speed digital circuits and the performance of analog blocks where noise is critical. Moreover, detailed device-level electro-thermal simulations are performed to explain the self-heating phenomena more in detail and to perform a comparison between bulk and FDSOI. The digital part of the self-heating study is performed on two very high-speed full-custom 64bit Kogge-Stone adders in 40 nm and 28 nm technologies. Thermal simulations are performed on these blocks to compare SHE in bulk and FDSOI geometries. The comparison of two implementations also provides the increasing significance of SHE with scaling. Extensive heating analyses are performed to find the most critical devices that are the primary heat generators. Design guidelines and solutions are proposed to flatten the temperature profiles in precharged and static logic implementations and to decrease the probability of electromigration. iii
Designing an efficient tidal turbine blade through bio-mimicry: a systematic review

Purpose This paper aims to conduct a comprehensive literature review in the tidal energy physics, the ocean environment, hydrodynamics of horizontal axis tidal turbines and bio-mimicry. Design/methodology/approach The paper provides an insight of the tidal turbine blade design and need for renewable energy sources to generate electricity through clean energy sources and less CO2 emission. The ocean environment, along with hydrodynamic design principles of a horizontal axis tidal turbine blade, is described, including theoretical maximum efficiency, blade element momentum theory and non-dimensional forces acting on tidal turbine blades. Findings This review gives an overview of fish locomotion identifying the attributes of the swimming like lift-based thrust propulsion, the locomotion driving factors: dorsal fins, caudal fins in propulsion, which enable the fish to be efficient even at low tidal velocities. Originality/value Finally, after understanding the phenomenon of caudal fin propulsion and its relationship with tidal turbine blade hydrodynamics, this review focuses on the implications of bio-mimicking a curved caudal fin to design an efficient horizontal axis tidal turbine.
Performance of Cooperative Maneuver Protocols in Real-World Automated Vehicles

Future automated vehicles will be able to negotiate cooperative maneuvers with each other via vehicle-to-everything communication, increasing safety, driving comfort, and traffic flow. Researchers have proposed several enabling cooperation protocols, but only a few have already been implemented in real-world vehicles. While simulations can be helpful for the development of a cooperation protocol, in the end, it is necessary to validate functionality in the target system. This paper implements one explicit general-purpose protocol for cooperative maneuvers in an automated vehicle, evaluating the overall cooperative, connected, and automated system in-depth. We show its feasibility, suitability for an example use case, and improvement potentials.
Finite-Time Distributed Control of Nonlinear Multiagent Systems via Funnel Technique

This article investigates the finite-time distributed adaptive consensus for nonlinear uncertain multiagent systems (MASs). The performance of the consensus errors can be prespecified in a funnel sense. Three achievements make this work depart from available results on prespecified performance and funnel control: first, a novel error transformation is constructed to prespecify the performance, which avoids the singularity problem pointed out in the literature in the differentiation of the control law; second, the funnel control method is extended in a MAS setting by suitably modifying the backstepping technique in a power integrator sense; and third, it is shown that finite-time stability notions can be attained without extra complexity being involved in the design. Stability analysis and comparative simulations demonstrate the method.
Hambatan Iran dalam Kerjasama Pembangunan Mobile Power Plant (MPP) di Indonesia

Iran dan Indonesia telah menandatangani kesepakatan kerjasama dalam bidang ketenagalistrikan pada 2016, dimana dalam salah satu poin kerjasama disebutkan bahwa Iran berkomitmen untuk membangun Pembangkit Listrik Bergerak atau Mobile Power Plant sebesar 5000 Megawatt (MW) dalam jangka waktu lima tahun sebagai bagian dari pencapaian target pemerintah Indonesia untuk memenuhi kebutuhan nasional sebesar 35.000 MW pada tahun 2020. Namun hingga berakhirnya periode kerjasama tersebut, Iran sama sekali belum melakukan langkah apa pun untuk pembangunan MPP ini. Pemerintah Indonesia tidak bisa berbuat banyak mengingat kesepakatan kerjasama ini sangat bergantung kepada Iran yang menjadi investor. Faktor-faktor eksternal dan internal seperti embargo ekonomi Amerika Serikat terhadap Iran pada 2018, keterlibatan Iran dalam konflik di Yaman, dan teknologi MPP yang relative baru dan masih dalam tahap perkembangan nampaknya menjadi hambatan bagi Iran untuk merealisasikan kerjasama yang disebutkan diatas.