Miracle-dz/UniFAR-FTUs huggingface dataset:
Budget-constrained Controller Placement in Software-defined Network

Software-defined network (SDN) provides an emerging platform for future Internet as it allows flexible reconfiguration of the network by separating the control programs (controllers) from the forwarding devices (switches, routers). The placement of controllers, and assigning switches to them is an important problem in SDN, since it depends on various constraints such as reduction of latency between nodes, placement cost, load of the network, and re-assignment of switches in case of failure. In this paper, we have formally formulated the controller placement problem in SDN, where our objective is to find a subset of locations for placing controllers, and assigning switches to them such that the maximum latency of the network is minimized within a specified budget. The problem is first proved to be NP-complete. Then we propose a genetic algorithm-based meta-heuristic approach to solve the problem. Our scheme is then extended to tackle fault tolerance, where the switches of a failed controller are reassigned to other active controllers. If reassignment causes an overload to the SDN, then a scheme is proposed to place a new controller in the SDN, while satisfying the load and budget constraints. The proposed algorithms are evaluated on various networks from the Internet Topology Zoo. The simulation results show that our approach can be effectively implemented in real-life SDN scenarios, such as data center networks, enterprise networks, and cloud-based networks.
Fully Fused Cover Song Identification Model via Feature Fusing and Clustering

In recent years, Cover Song Identification (CSI) based on Siamese Network and music representation learning has achieved good performance, however, there are still many problems such as limited feature fusion, missing decision threshold and single data label. In this paper, we propose a novel fully fused cover song identification model via feature fusing and clustering. In our proposed model, there are a fusion feature extraction structure, a channel separation decision structure, and a music feature clustering structure. First, we combine the pre-processing features of the dual input along the channel dimension to achieve full feature fusion and increase the fusion degree of the two songs in the feature extraction process. Secondly, we introduce channel separation to calculate multi-channel cross-features to improve the ability of the model to learn the difference between feature channels, and combined with the binary decision network to avoid the shortcomings of lack of decision thresholds in music representation learning. Finally, feature clustering generates invisible feature labels to enriches the types of cover data labels and reduces the difficulty of training. The model is trained in stages to optimize the clustering loss and the classification loss for cover and non-cover pairs, respectively. The model is validated on three public datasets, and experiments show that our model could achieve competitive results.
Using Text Mining to Evaluation for Online Shopping Rural Fruits and Vegetables

Online purchasing has become a new front in the conflict between agricultural products with the Internet's rapid expansion. Besides the cost saving because of no physical store, the adoption of Internet marketing is an inevitable trend. The development of several marketing strategies and physical channels has also changed traditional customers' purchasing habits. As user-generated information with analytical value grows rapidly, so does the percentage of unstructured data in the enormous data set. By mining unknown and hidden data of customer opinions, enterprises can gain valuable feedback and keep track of the benefits or drawbacks of products. Unstructured text mining technology is used for analysis in order to understand the factors that affect the evaluation of online shopping for rural fruits and vegetables. In order to achieve the sustainable development of agricultural products, this study examines the hidden issues buried beneath the heat of the rural revitalization plan using the unique fruit of the Maoming region, Shatangju, as the research object.
A DTN-based Spatio-temporal Routing using Location Prediction Model in Underground Mines

Situational awareness during any disaster depends on effective communication and location tracking. In the case of underground mines, where the communication methods are mostly central, the whole communication channel would be rendered unusable during a disaster. To this end, we propose the use of Delay Tolerant Networks (DTN) to allow the miners to function in a distributed manner and help in locating the injured miners and routing distress messages. Due to the unavailability of GPS signals, the pillar numbers are used to identify the locations of the miners. For spatio-temporal routing of messages, we formulate a new scheme using Contact Graph Routing (CGR) and GAE-LSTM. CGR forms routes based on the future contact (meeting times) of DTN devices, assumed to be known, which might not be the case in underground mines. Thus, we use GAE-LSTM, a graph-based deep learning model, which predicts the location of miners with time, based on the previous movement information, i.e. speed, time, angle, and location (in terms of pillar numbers) of the DTN nodes. The DTN nodes then search’s for other DTN nodes which will be at the same locations at same times. Using this information, a contact plan is formed which is used by the CGR for forming a route to send the messages.
Learned Load Balancing

Effectively balancing traffic in datacenter networks is a crucial operational goal. Most existing load balancing approaches are handcrafted to the structure of the network and/or network workloads. Thus, new load balancing strategies are required if the underlying network conditions change, e.g., due to hard or grey failures, network topology evolution, or workload shifts. While we can theoretically derive the optimal load balancing strategy by solving an optimization problem given certain traffic and topology conditions, these problems take too much time to solve and makes the derived solution stale to deploy. In this paper, we describe a load balancing scheme Learned Load Balancing (LLB), which is a general approach to finding an optimal load balancing strategy for a given network topology and workload, and is fast enough in practice to deploy the inferred strategies. LLB uses deep supervised learning techniques to learn how to handle different traffic patterns and topology changes, and adapts to any failures in the underlying network. LLB leverages emerging trends in network telemetry, programmable switching, and "smart" NICs. Our experiments show that LLB performs well under failures and can be expanded to more complex, multi-layered network topologies. We also prototype neural network inference on smartNICs to demonstrate the workability of LLB.
TreeChain: A High Throughput and Efficient Search based Secure Application for Internet of Vehicles

The Internet of Vehicles (IoV) rapidly expands due to lower bandwidth, enhanced security, and privacy for high-quality information sharing with minimal latency. To properly execute all these services, IoV must first overcome several challenges created by the centralized architecture, notably those related to throughput and data storage. We offer TreeChain, a high throughput and efficient information-searching technique based on a novel balance tree structure that also addresses the most extended chain sequence problem. Following that, TreeChain compares security strength in terms of several types of security attacks to other state-of-the-art methodologies, as well as performance results in terms of throughput and data storage. According to the results, TreeChain outperforms state-of-the-art solutions in terms of security and performance.
Hybrid Deep Learning CNN-Bidirectional LSTM and Manhattan Distance for Japanese Automated Short Answer Grading: Use case in Japanese Language Studies

This paper discusses the development of an Automatic Essay Grading System (SIMPLE-O) designed using hybrid CNN and Bidirectional LSTM and Manhattan Distance for Japanese language course essay grading. The most stable and best model is trained using hyperparameters with kernel sizes of 5, filters or CNN outputs of 64, a pool size of 4, Bidirectional LSTM units of 50, and a batch size of 64. The deep learning model is trained using the Adam optimizer with a learning rate of 0.001, an epoch of 25, and using an L1 regularization of 0.01. The average error obtained is 29%.
Greedy Algorithms for Finding Entanglement Swap Paths in Quantum Networks

The entanglement swap primitive facilitates the establishment of shared entanglement between non-adjacent nodes in a quantum network. This shared entanglement can subsequently be used for executing quantum communication protocols. The fundamental problem in quantum networks is to determine a path for entanglement swapping in response to demands for entanglement sharing between pairs of nodes. We investigate variants of this problem in this work. We propose a framework of Greedy algorithms that can be tweaked towards optimizing on various objective functions. In conjunction with a novel Spatial and Temporal (split across multiple paths) splitting approach to entanglement routing, we use this framework, which we call GST, to investigate the scenario when the demands are specified in terms of a starting time and a deadline. Considering the fragile nature of quantum memory, “bursty" demands are natural, and therefore the setting is important. We study the algorithm for maximizing the number of satisfied demands and the number of entangled pairs shared. We report empirical results on the performance against these objective functions, and compare with a naive algorithm that involves neither temporal and spatial splitting of the demands, nor the greedy approach to scheduling the demands.
Dynamic graph models inspired by the Bitcoin network-formation process

The network formation process in the Bitcoin protocol is designed to hide the global network structure: while most of the nodes of the network can be easily discovered, the existence of an edge between two nodes is only known by the two endpoints. In [Becchetti et al., SODA2020] the authors propose a dynamic random graph model inspired by the network formation process in the Bitcoin protocol and they prove that the evolution of the graph quickly terminates and that the resulting graph is an expander, with high probability. In this paper we extend the model in [Becchetti et al., SODA2020] to obtain dynamic random graph models that evolve forever: in the first model, edges can be faulty, i.e., each edge at each round disappears with some probability; in the second one, at every round new nodes join the network according to a Poisson process and each node currently in the network disappears with certain probability; in the third one, we consider a combination of the two models above, in which edges can be faulty and nodes can join and leave the network. We run extensive simulations to measure the “flooding time” in the three models, i.e., how long it takes a message starting at a random node to reach all, or almost all, the nodes. The simulations show that, for large ranges of the parameters of the models, the flooding time is short, i.e., compatible with a logarithmic growth, as a function of the number of nodes in the network. Our results also suggest that the default values of the network formation parameters used in the main implementation of the Bitcoin protocol seem overwhelmingly safe with respect to the stability of the network, and they might safely be tuned to reduce network traffic.
R&D Investment and Regional Balanced Economic Development: the Impact of Regional Innovation Network and Government Subsidies

This paper investigates the impact of government subsidies on research and development (R&D) investment and their wider economic effects. Moreover, this paper examines the moderating effect of regional innovation network level on the relationship between R&D investment and balanced economic development. The empirical analysis employs an econometric model using a panel of Chinese provincial data from 2014 to 2020. The results indicate that R&D investment has a significant single threshold effect on regional balanced economic development. As government subsidies exceed a first threshold value, the effect of R&D investment increases on regional balanced economic development. Regional innovation network level strengthens the positive effect of R&D investment on regional balanced economic development. The findings have important insights regarding the role of R&D investment in promoting regional balanced economic development in an innovative collaboration context and improve our understanding of the role of government subsidies for the relationship between R&D investment and regional balanced economic development.
Ionisation of He atoms by H+, He2+ and Li3+ nuclei

The cross sections, sigma i1 and sigma i2, of single and double ionisation of He atoms by H+, He2+, and Li3+ nuclei with energies ranging from 0.01 to 5 MeV amu-1 are calculated in the independent-electron approximation. The calculations are made for three versions of the probabilities of one-electron transitions to the continuum. The double ionisation of He by nuclei with energies E/A approximately 0.1-Z2 meV amu-1 is mainly due to the independent interaction of each atomic electron with the bombarding nucleus. The values of sigma i1 for all nuclei with E/A>or=0.2MeV amu-1 and of sigma i2 for He2+ and Li3+ nuclei with E/A=0.2-0.5 MeV amu-1 calculated in one of the versions are found to agree with the experimental values. The calculated values of sigma i2 for protons with E=0.2-0.7 MeV and 1.5-4 MeV are respectively higher and lower than the experimental values because the authors calculations were made neglecting the electron correlations.
Induced Alpha And Beta Electroencephalographic Rhythms Covary With Single-Trial Speech Intelligibility In Competition

Neurophysiological studies suggest that intrinsic brain oscillations influence sensory processing, especially of rhythmic stimuli like speech. Prior work suggests that brain rhythms may mediate perceptual grouping and selective attention to speech amidst competing sound, as well as more linguistic aspects of speech processing like predictive coding. However, we know of no prior studies that have directly tested, at the single-trial level, whether brain oscillations relate to speech-in-noise outcomes. Here, we combined electroencephalography while simultaneously measuring intelligibility of spoken sentences amidst two different interfering sounds: multi-talker babble or speech-shaped noise. We find that induced parieto-occipital alpha (7–15 Hz; thought to modulate attentional focus) and frontal beta (13–30 Hz; associated with maintenance of the current sensorimotor state and predictive coding) oscillations covary with trial-wise percent-correct scores; importantly, alpha and beta power provide significant independent contributions to predicting single-trial behavioral outcomes. These results can inform models of speech processing and guide noninvasive measures to index different neural processes that together support complex listening.
Neuronal miR-17-5p contributes to interhemispheric cortical connectivity defects induced by prenatal alcohol exposure

Prenatal alcohol exposure (PAE) is the leading cause of non-genetic intellectual disabilities in the Western world and is responsible of a wide spectrum of neurodevelopmental disorders referred to as Fetal Alcohol Spectrum Disorders (FASD). Structural and functional deficits in brain connectivity have been reported in FASD patients; still, whether and how PAE affects the axonal development of neurons and disrupts the wiring between brain regions is not known. Here, we developed a mouse model of moderate alcohol exposure during prenatal brain wiring to study the impact of PAE on corpus callosum (CC) development, a major white matter tract reported to be affected in FASD patients. Our results show that PAE induces aberrant navigation of interhemispheric CC axons that persist even after the end of the exposure, causing their ectopic termination in the contralateral cortex. Furthermore, these defects in interhemispheric connectivity persist into adulthood and are associated with defective bilateral sensorimotor coordination in behavioral tasks requiring cortical control and interhemispheric communication. Finally, we identified neuronal miR-17-5p and its target Ephrin type A receptor 4 (EphA4) as mediators of the effect of alcohol on the contralateral targeting of CC axons. Taken together, our results suggest that alteration of miRNA-mediated regulation of axon guidance signaling by prenatal alcohol exposure affects interhemispheric cortical connectivity and associated behavior in FASD.
International Scientific Colloquium

The drag force acting on a magnetic dipole due to the motion of a conducting rectangular bar in its field is computed by finite element analyses for different orientations of the dipole. The study is motivated by the novel techniques termed Lorentz Force Velocimetry (LFV) and Lorentz Force Eddy Current Testing (LET) for non-contact measurements of the velocity of a conducting liquid and for detection of defects in the interior of solid bodies. The present, simplified configuration provides important scaling laws and reference results for complete numerical simulations. The results of computations are also compared with analytical solutions for an infinite plate.
Assessment of structural behaviour of new L-asparaginase and SAXS data-based evidence for catalytic activity in its monomeric form

The present study reports the structural and functional characterization of a new glutaminase-free recombinant L-asparaginase (PrASNase) from Pseudomonas resinovorans IGS-131. PrASNase showed substrate specificity to L-asparagine, and its kinetic parameters, Km, Vmax, and kcat were 9.49×10-3 M, 25.13 IUmL-1min-1, and 3.01×103 s-1, respectively. The CD spectra showed that PrASNase consists of 30.9% α-helix and 69.1% other structures in its native form. FTIR was used for the functional characterization, and molecular docking predicted that the substrate interacts with serine, alanine, and glutamine in the binding pocket of PrASNase. Different from known asparaginases, structural characterization by small-angle X-ray scattering (SAXS) and analytical ultracentrifugation (AUC) unambiguously revealed PrASNase to exist as a monomer in solution at low temperatures and oligomerized to a higher state with temperature rise. Through SAXS studies and enzyme assay, PrASNase was found to be mostly monomer and catalytically active at 37°C. Furthermore, this glutaminase-free PrASNase showed killing effects against WIL2-S and TF-1.28 cells with IC50 of 7.4 µg. mL-1 and 5.6 µg. mL-1, respectively. This is probably the first report with significant findings of fully active L-asparaginase in monomeric form using SAXS and AUC and demonstrates the potential of PrASNase in inhibiting cancerous cells, making it a potential therapeutic candidate. HIGHLIGHTS A new L-asparaginase (PrASNase) was structurally and functionally characterized. SAXS revealed PrASNase is functionally active in monomeric form and oligomerizes with temperature rise. Monomeric PrASNase showed an IC50 value of 7.4 and 5.6 µg mL-1 against WIL2-S and TF-1.28 cells. Cytotoxicity of PrASNase against leukemic cell lines showed its potential as a biotherapeutic. GRAPHICAL ABSTRACT
Electrochemical deposition of Ag nanoparticles on ITO-coated glass: effect of different cyclic voltammetry scan rates on Ag deposition

Abstract This work aims to investigate the effect of different cyclic voltammetry scan rates on silver nanoparticles deposition on ITO-coated glass aiming to obtain improve its optical and electrical properties. The deposition was evaluated using different physical, chemical and optical characterization techniques, such as SEM, XRD, FTIR, UV–visible and electrochemical tests. Cyclic voltammetry was used to measure the Ag deposition at two different scan rates (20 CV at 10 mV/s and 100 CV at 30 mV/s). The characterization results have confirmed the formation of a homogeneous crystal layer of silver on ITO, which exhibited high transmittance in the visible light region. According to the cyclic voltammetry measurements, the outer spheres of the Ag NPs associated with the redox reaction illustrate the typical reduction and oxidation waves of Ag in the range of (−100 to +700) mV. The quantification using Cyclic voltammetry measurements has shown that the Ag nanoparticles deposition on ITO thin films are inversely proportional to the scan rates; in other words, higher deposition is obtained at lower scan rates.
Revealing the function of HMGB1 N-terminal acetylation by a protein semi-synthesis approach

HMGB1 (high-mobility group box 1) protein is a nonhistone chromatin-associated protein that has been widely reported to be a representative damage-associated molecular pattern (DAMP) and to play a pivotal role in proinflammatory process once it is in an extracellular location. Accumulating evidence has shown HMGB1 undergoes extensive PTMs that remarkably regulated its conformation, localization, and intermolecular interaction. However, the PTMrelated study has been dramatically hindered by the difficulty to access to homogenous proteins with site-specific PTMs of interest. Here, we introduce a protein semi-synthesis strategy via salicylaldehyde ester-mediated chemical ligations (Ser/Thr ligation and Cys/Pen ligation, STL/CPL). This methodology has enabled us to generate N-terminal acetylated HMGB1 proteins in high purity. Further studies revealed that the acetylation on N-terminus regulates its interaction with heparin and modulates its stability, representing a regulatory switch to control the HMGB1’s activity.
What is “Personal” About Personal Experience? A Call to Reflexivity for All

In their article, Nelson et al. (2023) raise concerns regarding the “paradox of experience” as it relates to the practice of bioethics. They argue that while experience provides individuals with intimate knowledge of events or states through which they have lived, it simultaneously creates the possibility for bias in the communication of that knowledge. The authors contend that personal experience is therefore due unique caution when presented as evidence in bioethical arguments. We agree with many of the concerns raised by this analysis. We also disagree with several elements, some of which we will not have the opportunity to address here. In this Commentary, we focus specifically on the rhetoric of “personal experience” on which the authors rely. We contend that their argument rests on an undertheorized conceptualization of personal experience, significantly weakening their claims. Though this topic has been taken up by a range of literatures relevant to bioethics—including philosophy, anthropology, history, and psychology—the authors provide no clear definition of this central component of their argument. As a result, their claims erroneously imply that a bioethics (and bioethicists) without such personal experience represents a proverbial tabula rasa, relatively free from bias. In place of such idealized dispassion, we encourage all bioethicists—regardless of their specific connections to “personal experience”—to cultivate a practice of critical reflexivity.