Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating Lambda-Term Reduction Complexity with Regression Methods

In this study, we dive deep into the Pure Lambda Calculus' evaluation process, specifically zooming in on term reduction steps. Commonly, these steps are seen as the same, but our research suggests otherwise - some steps are more complex than others. We investigated how specific term features play a role in computing efficiency, aiming to shed light on how different reduction strategies perform. Through testing, our Linear Regression and ANN regression models showed potential in predicting the time taken for a reduction step based on term details. Yet, when we used richer datasets to pin down complexity, our predictions didn't sharpen as much as hoped. This leads us to believe that term details, while useful, might not be the full puzzle pieces we need for spot-on strategy predictions. This research opens the door for further exploration into refining our models and unearthing other crucial factors that could shape term reduction time. Ultimately, our findings could help optimize programming tools like compilers and interpreters, steering them toward swifter operations.
Towards Software Development for Maintainability Evaluation of Business Process Models Using Fuzzy Logic

Business process modeling is a key tool in Business Process Management. Business process models are graphical representations of organizational activities used to depict ongoing tasks, events, and decision-making points to improve enterprise performance by gathering software requirements for workflow automation. Business process models facilitate the design of high-quality enterprise architectures, therefore, the quality of models impacts the success of proper solutions communication and implementation. Hence, this study focuses on the maintainability as one of the crucial quality attributes of business process models. Using the existing metrics of business process model complexity, this paper proposes to automatically detect complexity levels of business process models using the algorithm based on fuzzy logic and the respective software tool implementing the proposed algorithm. The software tool assumes treatment of business process models as the software engineering assets via the version-control system and its usage demonstrates processing of the large repository of business process models.
Technology for Synergistic Solutions Co-Creation Based on Multi-Agents' Diversities Interaction

When developing smart technology or devices, used whether in manufacturing, retail, health or other enterprises to create an efficient business, smart home or Internet of Things, it is necessary providing constructive feedback from the users. In addition to already known practice, an innovative approach is proposed: at the initial/early stages of developmental process the designers, developers, manufacturers, users, and stakeholders are simultaneously involved in the co-creation of multidisciplinary solutions. This approach allows initially using competencies and taking into account capacity, interests and expectations of all participants in order to make truly synergistic solutions, thereby optimizing the entire process and eliminating unnecessary steps and time consumption. For this purpose, a scientifically based technology for synergistic solutions co-creation by vast diversity of agents is proposed, which involves the identification of a system-forming factor, a structure-content pattern and mechanisms of co-creation. This allows modelling, control, piloting, multidimensional measurement, dissemination, multiplication and sustainable development of the whole process. Efficient technology of synergetic co-creation is based on an eco-humanistic model of multi-agents’ interaction, in which diversity is used as a driving force. The proposed approach can be used for synergistic interaction between human intelligence and artificial intelligence agents, which will create the prospect of eliminating the risk of confrontation, which is feared due to the intensive uncontrolled development of artificial intelligence. Such interaction can be supported by a system of digital techniques and tools, including: 1) database that self-develops in the process of multiagent interaction; 2) technique of superimposing ideal and real profiles of multiagent solutions; 3) interactive map of current state and changes based on decisions made; 4) continuum of multiagent solutions implementation with identification of terms and performers; 5) multiscale efficiency measurement system; 6) demonstrator of synergistic multiagent solutions at specific sites; 7) lighthouse: Atlas of interactive maps displaying the dynamics of sites development
Strategic Planning Technology: Application Aspects of Artificial Intelligence Linguistic Generative Models

A technology for strategic planning that enables the formal construction of realistic long-term plans using all available knowledge in various weakly structured domains (regional development, military sphere, business, IT, education, and so on) is proposed. This technology is based on group construction of a goal-oriented model of the subject domain, which is built through the decomposition of the main strategic goal and considers the temporal and resource characteristics of the system’s components, along with their interconnections. The usage of linguistic generative models of artificial intelligence is suggested at certain modeling stages. It is demonstrated that artificial intelligence tools contribute to enhancing the adequacy of subject domain models, thus improving the quality of recommendations generated by strategic decision support systems. Nevertheless, it is essential to note that these tools do not replace the involvement of expert teams in the modeling process. Instead, they serve as additional rather than primary tools in the strategic planning sphere.
SaFIC: A Spectrum Analysis Framework for Interferer Classiﬁcation in the 2.4 GHz Band

—We introduce the Spectrum Analysis Framework for Interferer Classiﬁcation (SaFIC), a prototyping framework for interferer detection in wireless networks. Robustness has become the major challenge in many application scenarios such as health care and industrial automation. Besides the assessment of the available channel capacity and node mobility, interference among different networks and even network technologies is one of the most critical and challenging problems. SaFIC provides an interface that allows for the implementation of protocol speciﬁc detector modules for different sources of interference. Based on a generic interface, the detection modules can schedule individual scan jobs that are served by the framework. Currently, we support TelosB motes running Contiki OS for the scanning of the frequency band. We developed detection models for WiFi and IEEE 802.15. 4 to demonstrate the capabilities of the framework.
Evidence to inform spatial management of a western Pacific Ocean tuna purse seine fishery

small subset of sets had 31 disproportionately large odontocete captures. Real time fleet communication and move-on rules 32 and avoiding sets on dolphin schools might reduce odontocete catch rates. Management of 33 informative operational predictors such as set association type and mesh size present additional 34 opportunities to balance catch rates of at-risk and target species. A transition to employing 35 output controls that effectively constrain the fishery would alter the spatial management strategy 36 to focus on zones with the lowest ratio of at-risk bycatch to target tuna catch. Findings inform 37 the design of alternative spatial management strategies to avoid catch rate hotspots of at-risk 38 species without compromising the catch of principal market species. 39
Bringing Packet Queueing to XDP

The Linux eXpress Data Path, or XDP, has found numerous uses in the industry, such as mitigating DoS attacks, load-balancers, and intrusion prevention systems. XDP provides a high-performance programmable network data path using the BPF framework and allows programmers to process packets early out of the driver. While XDP excels in forwarding packets, it currently has no mechanism for queueing or reordering packets and cannot implement traffic scheduling policies. In this paper, we present our ongoing work to address this challenge. We have designed a programmable packet scheduling extension for the XDP framework using recently proposed schemes for programmable queues. This extension allows programmers to define their packet schedulers using BPF while benefiting from the XDP fast data path.
The role of paranasal sinuses in the mysterious etiopathogenesis of idiopathic scoliosis

Aim: The paranasal sinuses play a role in maintaining postural balance and synthesizing neurotransmitters such as nitric oxide (NO). Although the etiopathogenesis of idiopathic scoliosis (IS) is not fully known, it is known that hormonal and neurosensory mechanisms play a role. In this study, the possible effects of paranasal sinuses on the etiology of IS were investigated. Methods: In this cross-sectional planned study, 38 individuals with IS (28 women and 10 men) and 39 healthy individuals (28 women and 11 men) were included. The right and left maxillary, sphenoid and frontal-ethmoid sinus volumes of the groups were calculated from magnetic resonance images. In addition, the entire head volume of each participant was calculated and the volume ratios occupied by all paranasal sinuses were calculated. Results: In the IS group, the right maxillary sinus mean volume decreased by 16% and the left maxillary sinus volume value decreased by 14% compared to the control group (p<0.01). No significant difference was detected between the groups in the sphenoid and frontal-ethmoid sinus parameters (p>0.05). It was observed that the values of left maxillary sinus volume, frontal-ethmoid sinus volume, right maxillary sinus ratio, left maxillary sinus ratio and frontal-ethmoid sinus ratio were lower in male individuals in the IS group than in healthy individuals (p < 0.01). Conclusion: In the presented study, it appears that the paranasal sinuses may have a role in the development of IS.
Design and Implementation of Low-Cost Blood grouping Prediction Using Machine Learning

One of the most common illnesses worldwide is diabetes. A blood sample from a finger prick is currently required for invasive methods. A tedious process must be repeated and increases the risk of infection. In this paper, a low-cost non-invasive detector for blood grouping assessment without blood sampling is proposed and constructed. The foundation of this non-invasive gadget is the NIR spectroscopy technology, which uses specialized short wavelengths to identify blood clumping molecules. The proposed technique is implemented using CNN based machine learning techniques like feature extraction and model training etc. By using this proposed non-invasive device, the patient can have a proper dose of insulin or other kind of medication and can control the Blood grouping during physical activities. The simulation is developed and shown for the identification of kinds blood group using machine learning model.
Reduced-Order Symbolic Admittance Model and Stability Analysis of Grid-Following Converters

In recent years, the penetration of power electronic devices in the power system becomes higher. Grid-following converter, as a main category of renewable energy interface in the power grid, may trigger stability problems under some circumstances. However, with the increasing complexity of control strategy and grid structure, stability analysis becomes a difficult task due to the heavy computational burden and vague physical meaning of the high-order small-signal model. Numerical order reduction methods cannot offer clear physical interpretation, and existing symbolic simplified models cannot cover a wide frequency band. To fill this gap, a reduced-order symbolic admittance model is proposed in this paper using bode diagram curve analysis and control loop decoupling, which is accurate in a wide frequency band (1~5000Hz). Based on this model, the corresponding RLC circuit model is subsequently constructed. At last, simulation and experimental results validate the effectiveness of the model.
Electric Performance of Various Shed Combinations for 330 kV Composite Insulation Material in Freezing Environments

Due of its outstanding pro capability, composites insulation has been utilized extensively by electricity generation companies globally. Nevertheless, at the same power threshold, the electrical efficiency of entirely cold composites barriers is less than those of the ceramic and glassy insulators due to more condensed sheds designs. Recent investigations have demonstrated that shelter design affects the electronic properties of icy composites separators. Five distinct kinds of 00330-kV composites separators with varied numbers of huge enclosures were utilized to assess their V50 resist power capabilities in order to increase the electrical efficiency of compound separators during freezing situations. In Sichuan College's multipurpose temperature room, the trials were conducted. The test findings demonstrate that by including large shelters, the V50 resist power may be raised. garden shed separation needs to be sufficient to prevent frost arching in order to achieve the greatest performance. The test findings also demonstrate there is no assurance that the presence of corona rings under severe icing conditions would result in an improvement in the electrical efficiency of composite separators. When coronal rings are required, it is important to carefully plan both their size and the layout of the roof.
The Analysis of Beta-Decay Radioactive Nuclides with Energy Spectra and Neural Network

This study applied machine learning using a neural network to analyze the nuclides from a beta-ray energy spectrum. Nuclides emitting gamma and alpha-rays are identified from the peak energy of their energy spectra. The shape of a typical beta-ray spectrum is continuous and has no apparent features such as the peak structure. This feature makes it difficult to identify nuclides from their energy spectra in the field. To overcome the challenge, we applied machine learning to beta-ray energy spectra. Five nuclides, 90Sr, 137Cs, 204Tl, 147Pm, and 60Co, were used for analysis to validate the method. A training dataset was created by Geant4 simulation. Beta-ray energy spectra were acquired using a Si-PIN photodiode detector to evaluate the performance of the machine learning in the nuclide analysis. The accuracy of the network training reached 90%. The evaluation confirmed that the trained neural network predicted the contribution of the five radionuclides within 26% on average.
A Semi-Supervised Autoencoder Approach for Efficient Intrusion Detection in Network Traffic

Intrusion detection systems (IDS) play a crucial role in protecting computer networks against cyber-attacks. An IDS typically recognizes known intrusion patterns or identifies unusual user behavior. With advancements in machine learning and deep neural networks, efficient intrusion detection solutions have been developed. In this paper, we present an autoencoder-based solution trained in a semi-supervised manner to detect anomalies in network traffic. The autoencoder is trained on normal network traffic, allowing the model to learn a compact representation of the regular traffic data. The model uses the reconstruction error as a mechanism for identifying anomalies in the network traffic. The proposed solution is designed to be efficient in terms of computational resources. The experiments were performed using the NSL-KDD dataset, a widely used benchmark for intrusion detection systems, and achieved an accuracy of 90.49% and an Fl-score of 91.81%, outperforming state-of-the-art methods in the same dataset.
A Novel High-Voltage Thyristor-Based Unipolar to Bipolar DC Power Grid Interface Converter

DC transformers (DCTs) with bipolar outputs typically serve as interfaces to interconnect grids with both unipolar and bipolar configurations. However, the voltage ratings of power switches limit the widespread adoption of fully controlled power electronic devices in conventional voltage balancers. Furthermore, the technique of series-connected power electronic switches also imposes constraints on the voltage rating of DCTs, impeding their practical application. This paper introduces an improved thyristor-based voltage balancer that has evolved from the conventional voltage balancer design. In this device, the adoption of thyristors enables the interconnection of medium/high voltage grids with differing voltage levels. The proposed topology is presented alongside an analysis of its operating principles. Additionally, the shutdown characteristics of the thyristors in this device are also investigated. Finally, the accuracy and feasibility of the proposed voltage balancer are verified through a MATLAB/Simulink model.
Modeling and Design of LCL-Filtered T-Type Inverters for Common-Mode Resonance Current Suppression in High-Power Applications

In the photovoltaic (PV) field, the three-level T-type inverter has largely been accepted as a promising candidate. However, it suffers from the leakage current problem, which can well be addressed by the modified LCL filter. Nonetheless, this modification brings a new challenge of common-mode resonance current (CMRC). To deal with this issue, several CMRC suppression control strategies have been proposed. In high-power applications with low-switching frequencies, the conventional CMRC suppression control strategy becomes invalid, and hence the system stability degrades. To resolve this problem, this paper introduces a delay compensated CMRC (DCCMRC) suppression scheme. In this paper, the simplified model of the modified LCL-filtered T-type inverter is derived. Later, a delay compensation method is proposed to eliminate the unfavorable effect of the digital control delay. Finally, the efficacy of the proposed methodology is validated through a set of simulations and experiments.
SMOG – An Explicitly Composable Memory Benchmark Suite for Heterogeneous Memory

In response to the increasing spatial and temporal heterogeneity of memory resources and their properties, we propose SMOG, a fully configurable and explicitly composable benchmark suite optimized for high-resolution time-series measurements. In systems with highly heterogeneous memory resources, or memory resources whose non-functional properties change dynamically, it is necessary to understand the response of different workloads and their memory access characteristics to the memory resources in order to make viable placement decisions. SMOG has been developed as a benchmarking tool that mimics the memory access behavior of real-world applications, which are typically composed of multiple concurrent workloads with varying memory access patterns, while simultaneously investigating the memory properties of the system under test by observing the response of the memory access behavior to the systems memory characteristics.
The Effect of Mismatch between SPECT and CT Images on Deep Learning-based Attenuation Correction for Myocardial Perfusion SPECT

Attenuation correction (AC) using deep learning (DL)-based methods improves image quality and quantitative accuracy in myocardial perfusion (MP) SPECT when CT-based AC (CT-AC) is not feasible. However, potential misalignments between SPECT and CT may affect the DL-based AC performance. This study aims to evaluate this effect based on real patient data. Fifity-eight stress/rest 99mTc-sestamibi scans from 34 patients were retrospectively analyzed. Thirty-six projections, acquired from RAO to LPO in 11 min, were reconstructed by OS-EM algorithm, with and without CT-AC. A 3D conditional generative adversarial network was implemented and optimized for the DL-ACµ and DL-AC, while 43, 5 and 10 datasets were used for training, validation and testing with a 6-fold cross-validation. SPECT and CT mismatch was modeled by randomly shifting the CT images in 3 dimensions (8.6±3.8 mm) with respective to their corresponding SPECT images based on a clinical reference. Mismatched SPECT and CT images were also used to train the network separately, i.e., mDL-AC and mDL-ACµ. NMSE, SSIM, joint correlation histogram and linear regression on a 3D cardiac VOI were compared for all cases, with matched CT-AC as the reference. NMSE and SSIM show significantly degraded performance on mDL-ACµ and mDL-AC as compared to DL-ACµ and DL-AC, respectively. mDL-ACµ is consistently better than mDL-AC. The joint correlation histogram and linear regression result is consistent. We conclude that though SPECT and CT mismatch significantly degrades DL-based AC performance, mDL-ACµ is more robust to mDL-AC for MP SPECT and shows improved results as compared to NAC.
Tightly-coupled Lidar-Visual-Inertial SLAM considering 3D-2D Line Correspondences

Multi-sensor fusion has been proven to be an effective way to achieve accurate and robust pose estimation in Simultaneous Localization and Mapping (SLAM) tasks, and how sensor observations are utilized directly affects the accuracy of pose estimation. However, fusing vision and lidar modalities is challenging due to their fundamentally different characteristics. Some works extract features from vision and lidar separately, while part of the extracted features are not stable enough and cannot be observed repeatedly, which reduces the performance of the odometry. Considering that line features are easy to extract with both cameras and lidar as simple geometric primitives of the environment, this paper proposes a tightly-coupled Lidar-Visual-Inertial odometry method considering 3D-2D line correspondences based on the LVI-SAM framework. This paper improves the front-end performance of the odometry by extracting, fusing, and tracking line features in the common field of view of lidar and cameras. It first extracts single-model line features in the Visual-Inertial-Odometry(VIO) and Lidar-Inertial-Odometry(LIO) subsystems respectively, then uses lidar line feature points set to assist in the depth recovery of visual line features, and uses line segment descriptors and 3D-2D re-projection to track features across multiple frames. In the pose estimation module, the stability score of line features is used for weighting. Experiments show that our method achieves an overall 10% pose estimation accuracy improvement on both the SUPS and M2DGR datasets.
Energy Efficient Thermal Management of 5G Base Station Site Based on Reinforcement Learning

The rapid development of Fifth Generation (5G) mobile communication system has resulted in a significant increase in energy consumption. Even with all the efforts made in terms of network architecture, system hardware design and device operation, its energy consumption and costs remain high. In order to control the operating environment within a reliable temperature range, the heating ventilation and air conditioning (HVAC) of 5G base station (BS) site consume a significant amount of energy for thermal management, and its operation still has great energy saving potential. This paper presents a three-stage approach of energy-efficient thermal management of 5G BS sites based on Q-learning and imitation learning. An imitation learning controller is proposed and a feature-controller library is constructed. Reliable initial control policies can be generated for new BS sites based on ensemble learning and rule-based constraints with this library. Furthermore, the optimal control policy for HVAC is learned using Q-Learning. This approach can be directly configured within the building baseband unit (BBU) and eliminates the requirement for additional sensors, facilitating practical engineering deployment. The application results show that the average energy cost of the HVAC system is reduced by 18.96% with the proposed approach, and the total cost of BS sites more than 10%.