Miracle-dz/UniFAR-FTUs huggingface dataset:
Splash-4: Improving Scalability with Lock-Free Constructs

Over the past three decades, the parallel applications of the Splash-2 benchmark suite have been instrumental in advancing multiprocessor research. Recently, the Splash-3 benchmarks eliminated performance bugs, data races, and improper synchronization that plagued Splash-2 benchmarks after the definition of the C memory model. In this work, we revisit the Splash-3 benchmarks and adapt them for contemporary architectures with atomic operations and lock-free constructs. With our changes, we improve the scalability of most benchmarks for up to 32 and 64 cores, showing an improvement of up to 9x in actual machines, and up to 5x in simulation, over the unmodified Splash-3 benchmarks. To denote the substantive nature of the improvements in the Splash-3 benchmarks and to re-introduce them in contemporary research, we refer to the new collection as Splash_4. 11GitHub repository: https://github.com/OdnetninI/Splash-4
An Inteligent Rate of Penetration Prediction Method Based on Lithology and LWD data

Rate of penetration (ROP) is an essential parameter in the drilling process, which directly affects the efficiency of drilling and the cost of drilling. With so much competition in the oil and gas industry today, ROP forecasting is becoming a major concern for the oil and gas industry’s future. ROP has many influencing factors, but most studies focus on the correlation between engineering parameters and ROP while ignoring the influence of formation lithology on ROP. To solve this problem, this paper investigates the correlation between geological information and ROP from lithology and well logging data, and achieves a more accurate ROP prediction by combining it with engineering parameters. In terms of the prediction model, this paper compares the traditional statistics-based method with the intelligent approach based on neural networks. Through quantitative evaluation and analysis, the intelligent method shows more accuracy and reliability than the conventional method in predicting ROP.
Load Optimization in Reverberation Chambers

Optimal loading conditions of a reverberation chamber were investigated. The relevant figure of merit is discussed and defined. Multiple loading configurations were tested and results are reported. A simple model of how loading affects measurement uncertainty was developed and used to find a loading configuration that vastly improve results with regards to the defined figure of merit compared to other configurations.
Research on Monster Path Planning Scheme in Horror Game Based on Whale optimization Algorithm

This paper proposes a path planning algorithm based on the whale algorithm, which when used with A* algorithm can effectively control path-finding progress. The algorithm introduces the Bezier curve, the algorithm can better describe the curve path between two points, to replace the logarithmic spiral in the spiral update part of the original whale algorithm. In addition, temporarily changing target point put the path planned by the algorithm far away from the shortest path, and appropriately surrounds the target point. This paper’s research object is the monster path-finding problem in horror games, and realizes the controllable non-shortest path planning under the special path environment. The algorithm planning result has better performance than the path planned by the A* algorithm.
ISAMod: A Tool for Designing ASIPs by Comparing Different ISAs

Designing the ISA (instruction set architecture) is a very critical activity in the entire ASIP (application-specific instruction set processor) design process. There is a long history of using automated tools that suggest custom instructions based on an analysis of the data flow graphs (DFGs) of target programs. Such approaches often create an ISA that is overspecialized for a small set of applications and they often suggest a plethora of custom instructions that cannot be practically implemented. A survey of recent work indicates that adding custom instructions to freely available ISAs such as RISC-V still relies on bespoke analyses and institutional memory. In this paper, we focus on such modern applications, where we only need to add a few instructions to an existing ISA such as RISC-V. The aim is to either supplant or complement the extensive manual analysis that goes into such decision making. We propose an unconventional approach that uses novel visualization techniques to first understand the impact of different ISA features by comparing the execution of the same program using different popular ISAs: both RISC and CISC. Our novel graphical methods provide simple and intuitive explanations for differences in performance across ISAs for the same micro-architecture. Moreover, we can use this information to pick desirable instructions from other ISAs and evaluate their impact when they are incorporated. We show examples where we are able to increase the average performance by 16.5 % for 6 SPEC-2017 benchmarks by just adding 2–10 extra instructions in the basic RISC-V ISA. The performance gain is comparable with state of the art custom instruction generators. Our tool, ISAMod (ISA Modify), achieves this using a very simple and intuitive approach. It has the potential to prove itself as a vital part of the overall design flow and can reduce reliance on institutional memory to a large extent.
Variability sources and reliability of 3D — FeFETs

Discovery of ferroelectricity (FE) in binary oxides enables the advent of FE memories and a plethora of novel CMOS compatible building blocks spanning from the logic domain to high-density storage and neuromorphic computing. In this paper we develop the first comprehensive model of vertical Ferroelectric Field Effect Transistor, V-FeFET, to identify sources of variability, understand retention problems, and point a path to improving reliability and enabling high-density storage FE memories with extended endurance.
Application on Bursaphelenchu Identification in Customs Based on Neural Network with Pluggable Attention Module

Bursaphelenchus is the key quarantine object of the customs quarantine department, but there are many kinds of Bursaphelenchus and their morphological changes are very complicated. Therefore, the identification of Bursaphelenchus has long been a difficult problem in the quarantine work of various customs. We propose a new identification method based on convolutional neural network with pluggable attention module. The method first uses the convolutional neural network to extract the features of Bursaphelenchus, and then obtains the attention map of the characteristic part of Bursaphelenchus through the pluggable attention module, adds the attention map to the identification network for training, and then strengthens the attention of different region weights, thereby strengthening the neural network to focus on learning the key feature regions of nematodes, so that the network can more accurately identify effective feature information. Compared with the traditional neural network model, the algorithm model proposed in this paper has achieved a high recognition accuracy rate and can effectively identify samples that are difficult to distinguish. The algorithm module proposed in this paper has been able to applied to the identification and classification of Bursaphelenchus in the quarantine department of Ningbo Customs.
On the BEP Analysis of M-QAM in a Frequency Non-Selective Beaulieu-Xie Fading Channels

In this paper, new and exact expressions are presented for the bit error probability (BEP) of M-ary quadrature amplitude modulation (M-QAM), considering a frequency non-selective Beaulieu-Xie (BX) fading channels, with and without spatial diversity. Special cases for the BX distribution, such as generalized Rice, non-central Chi and κ-μ distribution, are also analyzed in this work. The new BEP expressions are obtained by a method in which the division of the received signal by fading is performed, making the communication channel under fading corresponding to a channel subject to an additive noise. A new expression for the generalized moment generating function (MGF) for the signal-to-noise ratio (SNR) of the BX fading model is determined in this work. Several BEP curves as a function of SNR are presented under different parameters that characterize the channel, considering perfect or imperfect channel state information (CSI).
Classification of images by using TensorFlow

In the project of image classification, one model was built to sort images. By using the model which was built in the project, pictures can be classified effectively and quickly. At the beginning of the project, an appropriate data set was chosen. Then, the model was created by using TensorFlow. Next, the model would be trained to get the parameters with good fitting. Finally, in order to evaluate the model effectively, several graphs of validation accuracy were created. In the process of completing this project, the members of Team have mastered the ability to construct convolutional neural network models using python. What’s more, the members of the team also develop a good ability of data analysis.
Modeling Finite-Radius VHF and HF Wire-Antennas for Numerical Dosimetry Applications in Near-Field Interaction Scenarios

This article presents a study of the impact of finite-radius wire antennas in the High Frequency (HF) and the Very High Frequency (VHF) bands on human operators located in the near-field zone. Modeling a finite-radius wire antenna can be a very tricky task, since the antenna parameters, such as the input impedance and the radiated power depend on the wire’s shape and the air-gap, as well as its length and the input power source. However, to accurately model the wire shape and the air-gap a very fine resolution is normally needed since they are normally very small in size. In a nutshell, this article studies the impact of the previously mentioned antenna parameters on the Specific Absorbed Rate (SAR) distribution in humans and some possible techniques to speed up the simulation process.
The Transition From White Box to Black Box: Challenges and Opportunities in Signal Processing Education

Modern engineering education is increasingly assuming an interdisciplinary character, where developments in one area almost invariably affect other areas. A prominent example is that of signal processing, which has undergone significant changes with the emergence of machine learning (ML) and deep learning (DL) in recent years. While the impact of ML/DL is clearly visible from the viewpoint of research and development as well as industrial applications, it is not immediately clear how signal processing education should evolve in terms of pedagogy and content. Hence, the main purpose of this article is to provide some insight into this aspect. In particular, we emphasize that the introduction and popularity of ML/DL, especially at the level of teaching, has provided an opportunity to bring the focus back to some of the fundamental ideas rooted in signal processing and other related fields of study.
Compression Optimization For Automatic Verification of Network Configuration

In the era of big data and artificial intelligence, computer networks have become an important infrastructure, and the Internet has become ubiquitous. The most basic property of computer networks is reachability. The needs of the modern Internet mainly include cost, performance, reliability, and security. However, even for experienced network engineers, it is very difficult to manually conFigure the network to meet the needs of the modern large-scale Internet. The engineers often make mistakes, which can cause network paralysis, resulting in incalculable losses. Due to the development of automatic reasoning technology, automatic verification of network configuration is used to avoid mistakes. Network verification is at least an NP-C problem, so it is necessary to compress the network to reduce the network scale, thereby reducing the network verification time. This paper proposes a new model of network modeling, which is more suitable for the verification of network configuration on common protocols (such as RIP, BGP). On the basis of the existing compression method, two compression rules are added to compress the modeled network, reducing network verification time and conducting network reachability verification experiments on common networks. The experimental results are slightly better than the current compression methods.
Detection of Concurrency Errors in Multithreaded Applications Based on Static Source Code Analysis

Computer systems that allow multithreaded execution of applications have become extremely common, even small portable devices operate in multithreaded mode. This is undoubtedly very convenient for users, but for programmers it is associated with many unwanted errors, which can occur after writing application code. These errors include race condition, deadlock, atomicity violation and order violation. The subject of this work is related to the detection of these errors in the process of static software analysis. The paper presents the author’s model, which was then used to detect the above-mentioned occurrences, additionally each error has been discussed in detail. A tool supporting the detection of errors in multithreaded applications was also developed and the results of this tool were presented.
SIXOR: Single-Cycle In-Memristor XOR

With the fast approach of the end of silicon scaling and existing problems, such as the Von-Neumann bottleneck, alternative computing paradigms are in demand. In-memory computation (IMC) is one of the most promising solutions, and memristive technology is one of the best platforms for that purpose. Many logic families have been proposed to enable memristive IMC, among which <italic>stateful</italic> logic family stands out due to its minimal power consumption and simplicity. In this work, to complement existing works, we propose the first stateful crossbar-compatible <monospace>XOR</monospace> atomic logic operation that requires only one cycle for its completion, which is two times faster than the current minimum required time for performing <monospace>XOR</monospace> (which is two cycles) using other atomic operations in comparable memristive stateful logic families. We show that, in an example case of an adder, by taking advantage of the proposed single-cycle in-memristor XOR (SIXOR), up to <inline-formula> <tex-math notation="LaTeX">$4.5\times $ </tex-math></inline-formula> speedup can be achieved compared to other SoA stateful adders. The gained speed-up scales up in more complex systems and calculations that use <monospace>XOR</monospace>.
Deep Machine Learning Digital library recommendation system based on Metadata for Arabic and English Languages

During the last three decades, information technologies are adopted by many libraries. It provides public access to their material in digital form to improve service. Metadata are the key aspect that must be considered to achieve a proper integration of digital library. It is data about data and has many purposes: data description, data browsing and data transfer. The advanced search engine for text documents allowed retrieving text information in an efficient way. For the organization structured digital collections on internet scale, metadata is an approach for retrieval improvement, preservation, and interoperability. However, such engines experienced low accuracy when documents had unique properties that need specialized and deeper semantic extraction. By Combining the strengths of the deep learning models with that of word embedding is the key to high-performance metadata classification in digital library recommendation system. Throughout this article, we aim at providing a proposed method on the utilization of the deep machine learning approaches to build digital library recommendation system based on Metadata for Arabic and English languages.
Attribute-driven Topical Influential Users Detection in Online Social Networks

At present, online social influencers are guiding the recognition and behaviors of their connections by becoming a voice of moulding opinions. As a consequence, influential user detection has become unavoidable to explore the dynamic evolution of Online Social Networks (OSNs) for any new procedure either for viral marketing applications or administrating the propagation of producing information. Existing methods pay less concentration on the temporal factor of the users’ interests. Our intent is to detect influential users who express their interest towards a particular query on multiple topics at various time periods by spotlighting more on users’ latest activities. The suggested temporal TwitterRank based topical influential users detection in multi hop neighbors network (TIUDMNN) method is based on the addition of PageRank algorithm. We also estimate the outcome of indirect influence i.e. focusing both on users’ influence to their direct neighbors and considering neighbors who are multi hops (2 or 3 hops) away. We conduct experiments on real datasets to illustrate the potency and performance of the proposed approach.
Diabetic Retinopathy Detection Using VGG-NIN a Deep Learning Architecture

Diabetic retinopathy (DR) is a disease that damages retinal blood vessels and leads to blindness. Usually, colored fundus shots are used to diagnose this irreversible disease. The manual analysis (by clinicians) of the mentioned images is monotonous and error-prone. Hence, various computer vision hands-on engineering techniques are applied to predict the occurrences of the DR and its stages automatically. However, these methods are computationally expensive and lack to extract highly nonlinear features and, hence, fail to classify DR’s different stages effectively. This paper focuses on classifying the DR’s different stages with the lowest possible learnable parameters to speed up the training and model convergence. The VGG16, spatial pyramid pooling layer (SPP) and network-in-network (NiN) are stacked to make a highly nonlinear scale-invariant deep model called the VGG-NiN model. The proposed VGG-NiN model can process a DR image at any scale due to the SPP layer’s virtue. Moreover, the stacking of NiN adds extra nonlinearity to the model and tends to better classification. The experimental results show that the proposed model performs better in terms of accuracy, computational resource utilization compared to state-of-the-art methods.
A Deep Learning based Detector for FSO System Considering Imperfect CSI Scenario

In free space optical (FSO) communication system, the maximum likelihood (ML) is the optimum detector considering perfect channel state information (CSI) at receiver. However, the ML does not provide a proper performance in imperfect CSI scenario. Therefore, a deep learning based detection method is proposed in this paper. In this paper, we use deep neural network to get a mapping function of a received signal and transmitted symbol streams. Moreover, the end-to-end approach using deep learning with the conventional ML method is compared for both perfect and imperfect scenarios. Simulation results show that our method presents a better performance compared to the conventional ML method in imperfect CSI. Moreover, our proposed method reaches the same performance as the ML detector in perfect CSI.
Offset-Meandering Line Fed-Patch Periodic-Leaky Wave Antenna for Scanning through Broadside and Consistent Gain

In this work, a meandering line fed-patch periodic-leaky wave antenna (P-LWA) is designed to achieve broadside beam scanning and consistent gain response. The meandering line is provided a vertical offset (s) away from the longitudinal axis of the unit cell (UC). The combine effect of meandering in the feeding line and its offset acts as a self matching network that enables open stop-band (OSB) elimination at broadside. Consequently, there is no extra matching circuit used in the unit cell which facilitates easy analysis and optimization. The optimized broadside frequency (fB) for the P-LWA is 7.7 GHz. To verify the proposed concept, a prototype of the proposed P-LWA is fabricated and measured. The measured value of impedance bandwidth and maximum realized gain are 36% and 15.4 dBi, respectively.
Numerical Modelling of Glide Periodic Structures via Integral Equations

In this paper we propose to study the dispersion diagram of non-canonical glide-symmetric unit cells via integral equations with the use of the periodic Green’s function and discretized via the method of moment. The proposed approach is validated with a fully metallic implementation of a Luneburg lens operating at Ka-band with potential use for 5G communications. The promising preliminary numerical results highlight the potentialities of this approach.
Spatial Channel Model Validation for 3GPP FR2 MIMO OTA

Recently, 3GPP finished the study item on radiated metrics, and methodologies to verify MIMO performance of UEs. A key aspect of the methodology is the spatial channel model employed to illuminate the UE. It is paramount to verify that the test system creates the intended spatial channel model. Metrics to judge the quality of the generated testing environment will be described, which include Power Delay Profile, Doppler Autocorrelation, V/H, and Power Angular Spectrum Similarity Percentage.
Parallel matrix multiplication algorithms in Supercomputing

Nowadays high-performance computing is gradually implementing Exa-scale computing, and the performance of single node has reached several T-flops. Communication problem has become one of the main concerns of parallel matrix multiplication algorithms. This paper surveys the research on PMM algorithms on supercomputers around the world. We first stress the significance of PMM (Parallel Matrix multiplication) algorithms in high performance scientific computing, followed by a background introduction on widely employed PMM algorithms. We introduce the researches on parallel techniques for PMM algorithms from two aspects: classical parallel matrix multiplication and Strassen’s fast matrix multiplication. Further, we present the detailed complexity analysis comparison of each algorithm. Finally, we summarize the paper and discuss potential directions of future work.
Evaluation of Earthquake Resistance of Urban Buildings using Image Processing and Machine Learning Techniques

In this project, an approach has been taken to evaluate the earthquake resistance of urban buildings in Dhaka. An automated decision support system has been developed that takes the images of the buildings along with basic information as input. The system then outputs whether the building is at risk and requires structural evaluation. Data from 1106 buildings were collected during the project from 12 different areas of Dhaka. The output decision of the system is determined using a machine learning algorithm. Specifically, a CNN-based deep learning model was trained on the data collected during this project. Every deep learning model needs a baseline to make the predictions on. In this project, the baseline was developed from the FEMA P-154 report that deals with the visual screening of buildings to assess their risks during earthquakes. FEMA is the Federal Emergency Management Agency (FEMA) of the USA, a renowned agency that works with seismic hazards. After experimenting with different parameter combinations, the maximum accuracy achieved by the model was 71%. The latest deep learning models operate on millions of instances to make their predictions. Comparatively, our model was trained on only 1106 instances. With the introduction of more data points, we can achieve an accuracy of over 90% with this model.