Miracle-dz/UniFAR-FTUs huggingface dataset:
A Scalable Deadlock-Free Static Routing Algorithm for Chiplet-Based Systems

The utilization of the Chiplet methodology can accelerate VLSI system development and provide better flexibility. Building interconnection networks across multiple Chiplets and ensuring high-performance deadlock-free routing in systems with diverse irregular topologies is a challenging task.To avoid the reordering introduced by adaptive routing algorithms, a scalable static deadlock-free routing algorithm specifically designed for Chiplets is proposed. This approach capitalizes on static routing, a feature that ensures consistent message order due to fixed paths, fundamentally averting reordering issues. By adaptively configuring the state of the local router, it is possible to proactively initiate turns or exit detour loops, thereby effectively preventing deadlocks. Furthermore, by employing state configuration, the routing remains deadlock-free even when there are variations in the number of Chiplets, scale, or internal topology. This showcases the system’s scalability.Due to limited wiring resources, we chose classical routing algorithms up*/down* to compare, and the results showed a significant latency advantages, with a saturation injection rate approximately 1.5 to 2 times higher.
Some Experimental Results on the Statistical Properties of Least Squares Estimates in Control Problems

The statistical properties of the certainty equivalence control rule and of the least squares estimates generated by this rule are examined experimentally in a linear model with two unknown parameters. It is found that the least squares certainty equivalence rule converges to its true value with probability one and is asymptotically efficient, having an asymptotic distribution with a variance as small as any other strongly consistent rule. However, while a linear combination of the parameter estimates is consistent, the evidence does not confirm that the individual estimates themselves are consistent. If these converge to their true values at all, they do so very slowly (on the order of (log t)').
The D-allose operon of Escherichia coli K-12

Escherichia coli K-12 can utilize D-allose, an all-cis hexose, as a sole carbon source. The operon responsible for D-allose metabolism was localized at 92.8 min of the E. coli linkage map. It consists of six genes, alsRBACEK, which are inducible by D-allose and are under the control of the repressor gene alsR. This operon is also subject to catabolite repression. Three genes, alsB, alsA, and alsC, appear to be necessary for transport of D-allose. D-Allose-binding protein, encoded by alsB, is a periplasmic protein that has an affinity for D-allose, with a Kd of 0.33 microM. As was found for other binding-protein-mediated ABC transporters, the allose transport system includes an ATP-binding component (AlsA) and a transmembrane protein (AlsC). It was found that AlsE (a putative D-allulose-6-phosphate 3-epimerase), but not AlsK (a putative D-allose kinase), is necessary for allose metabolism. During this study, we observed that the D-allose transporter is partially responsible for the low-affinity transport of D-ribose and that strain W3110, an E. coli prototroph, has a defect in the transport of D-allose mediated by the allose permease.
Deep Learning-Based Model with XAI for Brain Tumor Classification and Segmentation Using MRI Images

Brain tumor classification & segmentation are crucial challenges in medical image analysis. Brain tumors are often incurable malignancies that arise from glial support cells. Medical study reveals that manual classification with human-assisted support might result in inaccurate prognosis and diagnosis because of the diversity and similarity of malignancies & normal tissues. Brain tumors are classified & segmented using T1, T1c, T2, and FLAIR MRI modalities. In this paper, we provide a technique for extracting brain tumours from 2D MRI images using several deep-learning tumor classification models (ResNet50, InceptionV3, VGG19, VGG16, DenseNet121, and EfficientNet). To give interpretability and explainability for the classification findings, the Lime (Local interpretable Model-Agnostic explanations) approach is also applied. During the segmentation step, we explore three widely used architectures: U-Net, SegNet, & ResUNet.The application of sophisticated classification models, XAI, and a fresh dataset establishes the framework for better tumor diagnosis and segmentation from 2D MRI images. In our experimental study, the ResNet50 model beat the other deep learning models, obtaining 96% accuracy. Furthermore, the suggested segmentation models ResUnet, Segnet, and U-Net have dice scores of 0.851, 0.772, and 0.7424, respectively.
Identification of Viable Listeria Species Based on Reverse Transcription-Multiplex PCR (RT-MPCR) and Restriction Digestion

A novel method for the identification of viable Listeria species was developed based on reverse transcription-multiplex PCR (RT-MPCR) and restriction digestion. The targets for RT-MPCR were iap mRNAs whose genes are common to all Liseria species. A set of five primers was used in this study. Two of them were genus specific, and the other three were specific to L. monocytogenase, L. innocua, and L. grayi respectively. By RT-MPCR, L. monocytogenese, L. innocua, L. grayi, and a group of Listeria species, including L. ivanovii, L. welshimeri, and L. seeligeri, were specifically identified. To differentiate the latter three Listeria species, RT-MPCR products were subjected to digestion with HpaI and ScaI. The sensitivity of RT-MPCR in detecting Listeria species was determined to be 50 CFU/mL. RT-MPCR was found to discriminate between viable and nonviable cells and to detect viable Listeria species in a food model.
Development of an open-source platform for calculating losses from earthquakes

Risk analysis has a critical role in the reduction of casualties and damages due to earthquakes. Recognition of this relation has led to a rapid rise in demand for accurate, reliable and flexible risk assessment numerical tools and software. As a response to this need, the Global Earthquake Model (GEM) started the development of an open source platform called OpenQuake, for calculating seismic hazard and risk at different scales. Along with this framework, also several other tools to support users creating their own models and visualizing their results are currently being developed, and will be made available as a Modelers Tool Kit (MTK). In this paper, a description of the architecture of OpenQuake is provided, highlighting the current data model, workflow of the calculators and the main challenges raised when running this type of calculations in a global scale. In addition, a case study is presented using the Marmara Region (Turkey) for the calculations, in which the losses for a single event are estimated, as well as probabilistic risk for a 50 years time span.
ATL: A Link Failure Recovery Method with Fast Recovery Speed, Low Interruption Rate, and Small TCAM Consumption in SDN

Based on the architecture of the Software Defined Network (SDN), we argue that SDN together with Internet of Things (IoT) applications can enhance the control and management of IoT in terms of flexibility and intelligence. However, with the rapid development of SDN, link fault tolerance has become a crucial challenge in ensuring network reliability and stability. In this field, the current solutions mainly consist of proactive and reactive schemes, but there are still limitations and challenges. The proactive solutions often waste significant TCAM resources, while the reactive solutions face longer fault recovery time. In order to solve the shortcoming of both solutions, and to address the issues of traffic interruption caused by insufficient link bandwidth after link failures, this paper proposes a method called Adapt To Link (ATL) which combines the greedy algorithm with Pro-VLAN technology (a proactive method which calculates a backup path and assigns a VLAN ID for each link before link failures), aiming to achieve efficient fault-tolerant recovery for link failures in the SDN data plane. In this algorithm, we utilize the Pro-VLAN method and pre-compute multiple backup paths for each link before a failure occurs. When a link failure occurs, we can swiftly switch the affected traffic to the backup paths automatically without the controller, ensuring continuous flow of network traffic. Additionally, we introduce a greedy algorithm to satisfy the requirement of elephant flows, thereby improving network resource utilization and performance. By combining these techniques and methods, our algorithm exhibits the characteristics of low interruption rate, minimal resource consumption, and high efficiency. The experimental results demonstrate that under high load conditions, the ATL scheme reduces the interruption rate by 72.4% compared to the proactive approach and by 54.9% compared to the reactive approach, respectively. The number of additional flow rules in ATL is only 21.95% of the proactive scheme, and the recovery time after a failure is only 21.72% of the reactive scheme. As a result, the ATL algorithm enables rapid and reliable network recovery in the event of link failures, and optimizes the utilization of network resources simultaneously, validating its feasibility and effectiveness.
Analysis of Performance and Optimization in MindSpore on Ascend NPUs

With the rapid advancement of artificial intelligence, the complexity and depth of deep neural networks continues to grow, placing higher demands on computational power. To meet these requirements, various manufacturers have developed specialized computing processors for the training process of deep learning, such as Huawei’s Ascend Neural Processing Unit (NPU). In order to fully leverage the capabilities of the Ascend NPU, Huawei has introduced the MindSpore deep learning framework. The computational performance of deep learning frameworks plays a critical role for developers. However, there is a lack of comprehensive research on the analysis of performance and optimization in MindSpore framework on the Ascend NPU, leading to a scarcity of relevant references for deep learning development utilizing MindSpore on the Ascend NPU. To address this gap, this study examined the performance and optimization of MindSpore on Ascend NPUs through detailed experiments involving diverse workloads and multiple analysis metrics. The analysis is conducted at three levels: operations, models, and techniques, investigating the operations configurations, performance bottleneck, along with techniques selections, within the training process of DNNs. Consequently, this study offers significant insights and guidance for DL researchers and practitioners using MindSpore on Ascend NPUs.
Mobile learning with gamification for Alquran memorization

Memorizing Alquran (containing over 6,000 sentences) requires interaction between students and teachers. One of the obstacles in memorizing Alquran is a lack of interest in the process of memorizing. This study applied gamification in mobile learning for memorizing Alquran in order to increase the fun factor. The test results showed that there were significant differences in learning outcomes between the experimental group and traditional group using t-independent test with a significance value of 0.008. Mobile learning with gamification also improves qualitative factors, such as usability, fun, and ease of use.
Assessment of Osstell ISQ’s reliability for implant stability measurement: A cross-sectional clinical study

Resonance frequency analysis (RFA) allows assess implant stability by measuring implant oscillation frequency on the bone. RFA is an objective and non-invasive method for implant stability measurement, although scarce evidence has been provided so far on its reliability. Objectives: Assess the Osstell ISQ system’s reliability (i.e., its measurement reproducibility and repeatability) by means of the intraclass correlation coefficient (ICC) as statistical method. Study Desing: Implants stability registers were completed by means of Osstell ISQ on 85 implants on 23 patients. Six measurements were completed on each implant by means of two different SmartPegs (types I and II); that is, three consecutive measurements with each transducer. Results: Average ISQ was 72.40, 72.22 and 72.79, and 72.06, 72.59 and 72.82 in the first, second, and third measurements with SmartPegs I and II, respectively. Equal values or differences below three ISQ points were observed in 52.9% and 62.4% of the cases with SmartPegs I and II, respectively. The intraclass correlation coefficient was 0.97 for both SmartPegs, and repeatability and reproducibility also reached 0.97 for both SmartPegs. Conclusions: The RFA system Osstell ISQ presents almost perfect repeatability and reproducibility after intraclass correlation coefficient analysis. Osstell ISQ measurements are highly reliable regarding reproducibility. Therefore, one measurement proves enough. Key words:Dental implants, RFA, ISQ, implant stability, Osstell.
Extracellular Release and Signaling by Heat Shock Protein 27: Role in Modifying Vascular Inflammation

Heat shock protein 27 (HSP27) is traditionally viewed as an intracellular chaperone protein with anti-apoptotic properties. However, recent data indicate that a number of heat shock proteins, including HSP27, are also found in the extracellular space where they may signal via membrane receptors to alter gene transcription and cellular function. Therefore, there is increasing interest in better understanding how HSP27 is released from cells, its levels and composition in the extracellular space, and the cognate cell membrane receptors involved in effecting cell signaling. In this paper, the knowledge to date, as well as some emerging paradigms about the extracellular function of HSP27 is presented. Of particular interest is the role of HSP27 in attenuating atherogenesis by modifying lipid uptake and inflammation in the plaque. Moreover, the abundance of HSP27 in serum is an emerging new biomarker for ischemic events. Finally, HSP27 replacement therapy may represent a novel therapeutic opportunity for chronic inflammatory disorders, such as atherosclerosis.
Detection and Analysis of DDoS Attack Using a Collaborative Network Monitoring Stack

In recent years, Distributed Denial of Service (DDoS) attacks have become common and have evolved into a standard method of targeting entities. These attacks can be used to make money or to damage the network and systems of an organisation. The majority of recent research in this field has focused on implementing various machine learning (ML) strategies to detect and thwart these attacks. Even though these techniques have a high rate of accuracy, they occasionally struggle to detect zero-day vulnerabilities or manage intense network traffic. This investigation shifts its focus from DDoS detection to the creation of a cost-effective network monitoring system. The intended solution will provide real-time visibility into a simulated network environment on Graphical Network Simulator-3 (GNS3) and incorporate a machine learning (ML) engine for enhanced DDoS detection via network monitoring tools such as Prometheus, Zabbix, and Grafana. This initiative seeks to provide organisations with a comprehensive, cost-effective solution that extends beyond DDoS mitigation by emphasising a more cost-effective and efficient monitoring system. During the implementation of the project, a few technical obstacles prevented the integration of certain components into the solution, as will be described in the paper. However, an integrated solution and collaborative design will increase the solution's ability to detect more sophisticated DDoS attacks.
A Lightweight Method of Myocardial Infarction Detection and Localization From Single Lead ECG Features Using Machine Learning Approach

Computerized myocardial infarction (MI) detection and localization can be useful for early prevention of its aggravation and related cardiac health complications. However, the published research either focuses on binary classification or implements complex classifiers for localization to achieve good accuracy. In this letter, the objective is to implement an 11-class MI localization system on resource-constrained hardware with low complexity and latency. A simple and optimized autoencoder-k-NN classifier has been used to achieve accuracy and F1-score of 99.74% and 99.20%, respectively, while evaluating single lead Electrocardiogram (ECG) features from the PTB-Diagnostic ECG database. A standalone hardware implementation with an ARM-v6-based controller resulted in a latency and runtime memory engagement of 0.48 s and 4.31 MB, respectively, to process 5 s ECG data. The present research can be useful for quick screening of MI for portable healthcare applications.
Byzantine-Resilient Federated Learning via Reverse Aggregation

Federated learning is known to achieve optimal performance, which is the highest performance achievable through central learning, only by sharing locally updated models with the central server. However, in practice, the performance of federated learning can be compromised by Byzantine failures. In this paper, we propose a new method for mitigating such backdoor attacks, namely reverse aggregation, that is robust in both data and model poisoning attacks from multiple adversarial edge devices. Unlike conventional methods that utilize the predefined number of compromised edge devices to handle the backdoor attacks, which may be unavailable in practice, proposed reverse aggregation works without any such prior information with the aid of small-sized globally shared data. Numerical results validate the advantage of the proposed method by showing improved performance compared to the conventional Byzantine-resilient federated learning methods, e.g., Krum, Trimmed mean, and Loss Function based Rejection. Further investigation compares the error rate of the proposed approach with exhaustive search, or complete enumeration method, to show that the proposed reverse aggregation has better computation efficiency with negligible performance degradation $(\sim 0.01{\%}$.
Analysis of 80 rooftop PV systems in the Tucson, AZ area

We present field performance measurements of 80 rooftop systems in the Tucson, AZ region. We describe a framework that enables identification the causes of system performance variations. We can distinguish between shading, module orientation, outages, and weather conditions from the performance data alone, i.e., without physical inspection of the system. The modules used in the systems that we study are predominantly from 2 manufacturers: Sunpower and Schott, with two types of modules from each manufacturer. We show that the variation in system performance due to these four module types is smaller than the variations due to other system-level effects. We discuss the distribution of de-ratings due to shading, and de-ratings due to clouds, as well as the significance and duration of outages. We also examine the effect of system age on annual final yields.
Genetic Algorithm for Energy Efficiency in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are a popular solution for several monitoring applications, due to their low cost and ease of deployment, while they can collect a plethora of data. To acquire such information WSNs consist of distributed sensor nodes, usually inch scale with limited energy resources. Since WSNs are formed in an ad hoc manner, the distribution of sensor nodes in a geographic location may not be known prior to the deployment of the nodes, hence, routing paths between nodes and a base station should be found. Finding routing paths requires energy while even with routing paths established, transmitting data between nodes will increase the total energy consumption in the network. When a node runs out of energy it may no longer be used as part of a routing path. Therefore conserving energy will prolong the node lifespan, increasing the lifespan of the WSN. In this work, a Genetic Algorithm (GA) is used as a clustering strategy to improve energy efficiency in a large-scale WSN. According to simulation results, GA can achieve better performance in terms of energy consumption and is a promising approach to extend the network lifespan.
Improving Disaster Communication with MP-NMSA: Message-Prioritized DTN Routing Based on Node Mobility and Social Attributes

In this paper, we propose a delay-tolerant network (DTN) based message transmission algorithm, Message Prioritized DTN Routing Based on Node Mobility and Social Attributes (MP-NMSA), for disaster scenarios in which traditional communication devices and wireless devices are damaged, resulting in limited conventional communication technologies and wireless bandwidth congestion. First, machine learning techniques are utilized to process messages in disaster scenarios to achieve transmission prioritization of messages. Then, a mobile self-organizing DTN network is established to achieve message delivery in disaster environments based on the mobility and other social attributes of nodes. The experimental results show that MP-NMSA significantly improves delivery probability, average delay, and network overhead compared with traditional algorithms.
Pattern-Based Refactoring Process of Sequential Source Code

Software refactoring is a very well-studied subject, but with the huge gap between omnipresent multicore processors and the vast majority of software that has not been developed with multicore in mind, it gains new and important significance: At what locations should the sequential code be refactored? How should this be done for a given location? Is the code still correct? And finally: Does it execute faster? In this paper we present a refactoring concept to exploit parallelizable regions in legacy software. Our concept relies on the presence of recurring patterns and identifies potential regions, transforms them to parallel versions, tests them for correctness and tunes their parallel performance. We show early implementation results.
SLCSA: Scalable Layered Cooperative Service Attestation Scheme in Cloud-Edge-End Cooperation Environments

In a cloud-edge-end cooperation environment, edge and core cloud services are complementary and synergistic, jointly processing a large amount of private data uploaded by users. To prevent the leakage of private data, users must ensure that services are secure and trusted through remote attestation. Traditional one-to-one remote attestation schemes are typically used to test the cloud services. However, as the cloud platform scales and the number of edge and core cloud services grows rapidly, the traditional attestation method has problems, such as poor scalability and low attestation efficiency. Thus far, there has been a lack of feasible methods for users to verify multiple related services in a cloud-edge-end cooperation environment quickly. This paper presents a scalable layered cooperative service attestation (SLCSA) scheme, the first secure and scalable protocol for the efficient attestation of multiple cooperative services. The SLCSA scheme is based on a Boneh–Lynn–Shacham (BLS) multi-signature to improve the scalability of the scheme while enabling users to conduct the batch verification of services. We also analyze the security of the proposed scheme. To evaluate the proposed scheme, we implement it using Intel SGX, which can provide basic hardware-assisted attestation and a trusted execution environment for services. The experimental results show that the SLCSA scheme is practical and efficient in a cloud-edge-end cooperative environment.