Miracle-dz/UniFAR-FTUs huggingface dataset:
CodedINT: Leveraging Network Coding to Improve the Visibility of In-band Network Telemetry (INT)

With momentum gained from programmable data plane (PDP), in-band network telemetry (INT) has been widely considered as a promising technique for realtime network monitoring. In this work, we leverage network coding (NC) to design CodedINT, for improving the visibility of INT in lossy networks. Specifically, we propose to encode the telemetry data in multiple packets with NC and distribute the encoded data over a group of packets. Then, among the group of packets, if we can receive enough ones that satisfy the decoding condition of NC, the whole original telemetry data can be recovered. We first explain the design of CodedINT to elaborate on its operation principle, packet format, and system implementation. Then, we implement and experimentally evaluate CodedINT in a real network testbed. Our experiments demonstrate that in a lossy network with packet loss rate at 80%, CodedINT ensures that 89.48% of the telemetry data carried by INT packets can be recovered successfully.
From Machine Learning Based Intrusion Detection to Cost Sensitive Intrusion Response

Machine learning (ML) based intrusion detection systems (IDS) are increasingly used to discover abnormal patterns in network data and predict cyberattacks. However, the construction of intrusion response systems (IRS) used to deploy countermeasures and prevent malicious activities is more challenging because they require in-depth understanding of attack patterns, attacker behavior, and the correlation between different types of attacks. Furthermore, IDSs generate a large number of false positives and the confidence with which an attack can be predicted is usually unknown. As a result of these challenges in IDS and IRSs, inappropriate actions may be deployed, which may reduce network performance and users’ ability to perform typical tasks. Therefore, the present work proposes an intrusion detection and response method based on the Calibrated Random Forest (CRF) algorithm to overcome the key challenges related to the construction of an efficient IRS. The proposed CRF is used to quantify uncertainty in the prediction of cyberattacks and expresses each attack as a probability distribution. Subsequently, the predicted probabilities are used as confidence scores and integrated with domain expert knowledge for decision making in an IRS. We then use publicly available intrusion detection data sets to test and evaluate the proposed method based on three metrics: log loss, Brier score, and expected calibration error (ECE). Experimental results show that the proposed method makes intrusion response more reasonable and cost-sensitive, and has the ability to manage criticality, integrate domain knowledge and explain model behavior. It also demonstrates that this method provides an effective solution for security analysts in how to appropriately deploy and prioritize actions.
Derivation of the initial luminosity function and the past rate of star formation

The initial luminosity function ^(M v ) was introduced by Salpeter [ i ] . He assumed uniform formation of stars and derived the initial luminosity function from the observed main-sequence luminosity function and the life time of a star of magnitude M v on the main sequence. Recently van den Bergh [2] considered the depletion of the interstellar gas by star formation. He found that at a constant rate of star formation the gas in the solar vicinity will be exhausted about 7 X i o 8 years from now. It would seem very probable that the rate of star formation will depend on the amount of gas available, i.e. the gas density, and we will assume that the rate of star formation varies with the n-th power of the gas density. Direct evidence is available in the distribution of OB stars perpendicular to the galactic plane, which is narrower than the distribution of the gas. The gas has a dispersion of 140 psc while 184 southern cepheids observed by Walraven, Muller and Oosterhoff [3] have a dispersion of 65 psc. The difference in distribution may be explained if the rate of star formation varies with the square of the gas density, i.e. if n = 2. So :
Improving ART Pregnancy Rate with Two Kinds of Media and Two Types of Incubators

Culture media and incubators have played a key role in embryo quality. Here, we observed individual patient’s embryos to have different response for media and incuba tors. Patient’s 1850 zygotes were divided into two groups randomly and were cultured in Global and in P1 medium. The cleavage rate and embryo quality were recorded. The result showed that the cleavage, top quality embryos on Day 2 and Day 3 were not statistically different between media. However, 45% patient’s embryos grew very well in both Global and P1. 22% patient’s embryos grew well only in Global but poor qual - ity in P1, while 21% grew well in the Global but poorly in the P1. Only 12% patient embryos did not grow well in both. The pregnant rate was only 40% in P1 or 42.5% in Global (P>0.05). However, when two media were used simultaneously, the pregnant rate increased to 70.1%. Also, two incubators showed significant higher pregnant rate than in single incubator (73.2% vs. 60%, P<0.05). In conclusion, the favorable response of individual patient’s embryos to media and incubators suggests that using two media and two incubators for embryo culture could significantly improve embryo quality and pregnant rates.
A Blind Load-Balancing Algorithm (BLBA) for Distributing Tasks in Fog Nodes

In the distributed infrastructure of fog computing, fog nodes (FNs) can process user requests locally. In order to reduce the delay and response time of a user’s requests, incoming requests must be evenly distributed among FNs. For this purpose, in this paper, we propose a blind load-balancing algorithm (BLBA) to improve the load distribution in the fog environment. In the proposed algorithm, the mobile device sends a task to a FN. Then, the FN decides to process that task using the Double- Q -learning algorithm. One of the critical advantages of BLBA is that decision-making on tasks is done without any knowledge of the state of neighbor nodes. The proposed system consists of four layers: (i) IoT layer, (ii) fog layer, (iii) proxy server layer, and (iv) cloud layer. The experimental results show that the proposed algorithm with proper distribution of tasks between nodes significantly reduces the delay and user response time compared to the existing methods.
Throughput Optimization for SGF-NOMA via Distributed DRL with Prioritized Experience Replay

In this paper, we propose a novel distributed resource allocation mechanism for semi-grant-free non-orthogonal multiple access (SGF-NOMA) transmission to maximize the network throughput, where multi-agent deep reinforcement learning with prioritized experience replay (PER) is employed. We design a centralized training framework and decentralized decision making to increase the flexibility of the proposed scheme. More specifically, each grant-free user as an "agent" learns the dynamics of the environment and makes its decisions independently in a decentralized manner. No heavy information exchange is needed to find the optimal transmit power and sub-channel that maximize the throughput. Numerical results show that the proposed algorithm with PER enhances the learning efficiency compared to the algorithm with conventional replay buffer and outperforms the existing scheme with a 12% throughput increase.
Loss and thermal analysis of a 300 kW fast charging station

This report presents the thermal modelling of a 300kW fast charging station. It includes details of steady-state loss calculation of single phase dual active bridge (DAB) converter and estimations of steady-state loss of three-phase ACDC boost converter, selection of cold plate, and finite element method (FEM) analysis of the system based on the calculated loss. The thermal performance analysis of the system based on the FEM simulation shows that having 8 liter per minute (LPM) liquid flow with maximum 50 C inlet temperature can maintain the maximum junction temperature of the MOSFET chip below 110 C throughout the normal operating range with maximum of 314 mbar pressure drop.
A Precoding Scheme for Polar Coded Uplink MU-MIMO Systems

Multi-user multiple-input multiple-output (MU-MIMO) is one of the key techniques to meet high spectral efficiency requirements for the fifth generation (5G) and beyond 5G wireless network. To achieve the high capacity of uplink MU-MIMO, a generalized polar precoding scheme based on rotation and permutation (GP-RP) is proposed in this paper, which can enhance the reliability distinctions between users’ channels. Via the three-stage channel transformation, the binary polar coding, signal modulation and the original multi-user channel are successively partitioned into multiple bit polarized channels. To further amplify the polarization effect, an unitary precoding scheme is proposed for the uplink MU-MIMO system without changing the communication capacity. The simulation results show that the proposed precoding scheme can further improve the polarization between the synthesized user channels and achieve lower block error ratio (BLER).
Sensory Processing Sensitivity and Gastrointestinal Symptoms in Japanese Adults

Background: Sensory processing sensitivity is a personality or temperamental trait defined as individual differences in the tendency to perceive and process both positive and negative stimuli and experiences. Studies have shown that high sensitivity is correlated with psychosocial health, including depression and anxiety. However, its relationship with physical health has not been clarified. To fill this gap, using a large sample size with sufficient statistical power, an adult sample not including university students, and a range of covariates, this study examined the association between gastrointestinal symptoms as an indicator of physical health and sensory processing sensitivity. Methods: In this cross-sectional study, the participants were 863 Japanese adults (female = 450; male = 413; Mage = 30.4 years; SD = 4.9) who completed a web-based questionnaire. We statistically controlled for sociodemographic characteristics and examined whether sensory processing sensitivity is correlated with gastrointestinal symptoms. Results: The results showed that highly sensitive individuals were more likely to experience a wide range of gastrointestinal symptoms in the past week, including reflux symptoms, abdominal pain, indigestion symptoms, diarrhea symptoms, and constipation symptoms, even when statistically controlling for the participants’ sociodemographic characteristics. Conclusions: Our findings suggest that high sensory processing sensitivity is associated with physical health. Some of the potential causes of this are also discussed.
Learning the Optimal LTP Segment Size for Minimal Turnaround Times

Delay-tolerant networking (DTN) provides the mechanisms needed for both single and multihop space communications when cyclical bandwidth is available between the nodes. Because node contacts can be infrequent, achieving high bandwidth utilization during contacts or extending their duration as possible are topics of special relevance. In this work, an online approach for the Licklider Transmission Protocol (LTP) that is suitable for small size, weight, and power (SWaP) devices is suggested. The method dynamically searches for the optimal segmentation, i.e., the maximum payload size to be used to transmit data blocks. It is contrasted with a Q-learning-based method, which achieves similar performance with larger space complexity. Either option attains reliable bundle transmissions with acceptable delay and throughput levels well beyond the range achievable with static payload lengths and adverse conditions of high loss rates. The approach has minimal information requirements and does not require knowledge of the current bit-error-rate, transmission rate, modulation type, power, gains, nor the one-way light times. The method also requires minimal changes to the sending engine and is fully compatible with standard LTP receive engines. The technique can help to improve the effective DTN capacity by extending the usable contact times and achieving transmissions with improved performance when signal fluctuations occur.
Place attachment in stigmatized settings: a study based on community experience in Costa Rica (Apego al lugar en contextos estigmatizados: un estudio desde la experiencia comunitaria en Costa Rica)

ABSTRACT Research into place attachment theory has traditionally focused on the positive values associated with territories, leaving to one side the analysis of phenomena where the construction of socio-physical bonds is marked by ambivalence, as in the case of territorial stigma. With a view to contributing further information to existing knowledge on this topic, the research presented here aims to explore the dimensions involved in place attachment in relation to stigmatized urban communities in Costa Rica. This study applies a qualitative approach, carrying out interviews and applying participatory mapping. The results have been grouped into five dimensions and three maps, which show that place attachment in the communities studied here is marked by socio-affective bonds, the socio-demographic characteristics of their residents, urban centrality, territorial management and criminality. Based on the information generated, we conclude that place attachment is a complex phenomenon and is shaped by socio-political attributes that require more in-depth exploration.
A study of nonmonotonic reasoning

This thesis contains several essays on nonmonotonic reasoning, which touch on a number of key issues in that area. It begins with the introduction of the logic GK of Grounded Knowledge as a uniform semantic basis for fixed-point nonmonotonic logics. These logics include Reiter's default logic and Moore's autoepistemic logic, and to our knowledge GK is the first semantic unification of the two. Similarly to circumscription, GK is based on the notion of logical minimization, and thus provides a bridge between circumscription and fixed-point nonmonotonic logics, an outstanding problem in nonmonotonic logics. As an application of this bridge, we propose a formalization of logic programs with negation-as-failure in circumscription. Together with the unique names assumption, the formalization provides a concise representation for the facts that are true in every answer set of a logic program. We then introduce the notion of argument systems as a proof theory for nonmonotonic reasoning. By reformulating some major existing nonmonotonic logics as argument systems, we show that most nonmonotonic reasoning can be captured with the aid of a generalized negation-as-failure rule. We describe an implemented system for default logic based on this idea. Finally, we apply nonmonotonic logic to the formalization of inheritance and of theories of action, the two areas that have to a large degree motivated research in nonmonotonic reasoning. We show that various intuitions about nonmonotonic inheritance hierarchies can be conveniently formalized in logic programs with negation-as-failure. For reasoning about action, we first propose a formal yet intuitive criterion by which to evaluate the adequacy of theories of action. We then formulate a class of monotonic theories that satisfy the criterion by using explicit frame axioms. Finally for a significant subclass of the monotonic theories, we provide provably-correct nonmonotonic counterparts which avoid explicit frame axioms.
Parthenocarpy in Cucumis sativus L. as Affected by Genetic Parthenocarpy, Thermo-photoperiod, and Femaleness1

Parthenocarpic fruiting of genetically parthenocarpic and non-parthenocarpic pickling cucumber lines was determined under different thermo-photoperiods. The genetically parthenocarpic line, MSU 364G, produced both earlier and more fruits under all thermo-photoperiod treatments than the genetically non-parthenocarpic line, Gy 3. This was especially true at high night temperatures (18°C). Thus, maximum selection pressure for yield in genetically parthenocarpic lines might be best exerted under high night temperatures. Conversely, the production or yield of parthenocarpic fruits was greatest under low night tempeartures (12°C). Hybrids involving either of these 2 parental lines and 2 hermaphroditic lines were intermediate for parthenocarpic yield. Yield of parthenocarpic lines was associated with intensity of femaleness, i.e., strong femaleness resulted in earlier fruiting and greater numbers of parthenocarpic fruits. The development of parthenocarpic pickling cucumber cultivars for once-over mechanical harvest seems practical by combining parthenocarpic with gynoecious genotypes.
SASA: Source Address Spoofing Avoidance Mechanism under High Movement for Mega-Constellations

The emergence of mega-constellations is the most promising satellite network trend in recent years, which brings new security challenges to the network layer and higher layers, such as DDoS, worm, and DNS pollution. Source address validation is one of the effective solutions in terrestrial networks, by filtering the invalid address and resisting the source address spoofing. Because of the time-vary topology in mega-constellations, the source address validation mechanism faces the severe problem of the anchor mobility, which leads to a sharp decline in SAVI (Source Address Validation Improvements) performance and increases the cost to maintain the user status. In this paper, we develop a source address spoofing avoidance mechanism under high movement (SASA) for mega-constellations. Specifically, we propose that the user and the satellite both maintain the user status. After the satellite signed the binding information by the private key, it forms the mapping between the authenticity of the user address and the initial access satellite on the user side. Moreover, when the handover occurs, the user safely transmits the authentication information to the new access satellite through asymmetric encryption to complete rebinding. Simulation results show that SASA can greatly reduce the rebinding cost of mega-constellations by 95.04% in Starlink and 81.84% in Kuiper.
Optimizing replacement time for mining shovel teeth using reliability analysis and Markov chain Monte Carlo simulation

Purpose The purpose of this paper is twofold: an approach is proposed to determine the optimum replacement time for shovel teeth; and a risk-quantification approached is developed to derive a confidence interval for replacement time. Design/methodology/approach The risk-quantification approach is based on a combination of Monte Carlo simulation and Markov chain. Monte Carlo simulation whereby the wear of shovel teeth is probabilistically monitored over time is used. Findings Results show that a proper replacement strategy has potential to increase operation efficiency and the uncertainties associated with this strategy can be managed. Research limitations/implications The failure time distribution of a tooth is assumed to remain “identically distributed and independent. ” Planned tooth replacements are always done when the shovel is not in operation (e.g. between a shift change). Practical implications The proposed approach can be effectively used to determine a replacement strategy, along with the level of confidence level, for preventive maintenance planning. Originality/value The originality of the paper rests on developing a novel approach to monitor wear on mining shovels probabilistically. Uncertainty associated with production targets is quantified.
Adversarial Examples Generation Through Attacking the 1 × 1 Convolution Operator

Convolution neural networks (CNN) are vulnerable to adversarial attack, which brings security risks to the application of convolutional neural networks in reality. Adversarial attacks are usually done by adding subtle perturbations in images to fool models to output wrong results. In black-box conditions, the transferability of attack method is mostly focused to fool more model. To enhance the transferability of adversarial attack, we proposed an attack method against the commonly used $1\times 1$ convolution operator (1ConvA, $1\times 1$ Convolution Attack), which exists in many convolutional networks for improving the complexity of the model and the gradient flow. Experiments on many CNNs demonstrate 1ConvA can generate adversarial examples with better transferability.
Operational and environmental decisions for a two-stage supply chain under vendor managed consignment inventory partnership

Supply-chain-based organisations are nowadays facing intense pressure to abide to environmental regulatory requirements while they are striving to be responsive to customers’ needs at the least cost possible. As supply chain activities are among the top contributors to carbon emissions, several recent research works have investigated the impact of carbon regulation policies on economic and environmental supply chain performance. This paper presents integrated economic and environmental models for the one-vendor one-buyer supply chain problem under a vendor managed consignment inventory (VMCI) arrangement. Through the developed models, we study the impacts of two carbon reduction policies, namely carbon cap and carbon tax policies, on supply chain wide costs and carbon emissions. We first provide a much simpler and more compact formulation for the basic single-vendor single-buyer supply chain under VMCI agreement. We also present an environmental-based VMCI model where reduction of carbon footprint is considered as the only objective function. We then extend these two basic models to include each of the two carbon emissions reduction policies. We identify structural properties for the optimal solutions of the two hybrid economic and environmental models and propose algorithms to generate optimal solutions. The results of the computational experiments reveal that the implementation of carbon reduction policies, through carbon cap and carbon tax, may call for the adjustment of the vendor’s production and buyer’s delivery quantities to reduce carbon footprint without significantly increasing the operational costs.
Time-series metrics applied to land use and land cover mapping with focus on landslide detection

Abstract. Landslides are a recurring phenomenon in Brazil and have caused many socioeconomic losses and casualties. To monitor them, land use and land cover (LULC) and landslide inventory maps are essential to identifying high susceptibility areas. In this sense, the main aim of this study is to produce LULC classification focused on landslide detection via semi-automatic methods, using data mining techniques with remote sensing time-series imagery. For that, different indices, such as the normalized difference vegetation index, the normalized difference built-up index (NDBI), and the soil adjusted vegetation index were extracted from Sentinel-2 imagery. Basic, polar, and fractal metrics were extracted from the time series. From the Shuttle Radar Topography Mission digital elevation model, six geomorphometric features were extracted. Then, classification was performed with random forest with four different approaches: mono-temporal, bi-temporal, metrical, and all. In every approach, the NDBI index or metric derived from it presented the highest importance, and the slope was ranked among the six first predictors. The all approach showed the highest overall accuracy (OA) (88.96%), followed by metrical (87.90%), bi-temporal (82.59%), and mono-temporal (74.95%). Briefly, the metrical approach presented the most beneficial result, presenting high OA and low levels of commission and omission errors.