Miracle-dz/UniFAR-FTUs huggingface dataset:
Demystifying the Soft and Hardened Memory Systems of Modern FPGAs for Software Programmers through Microbenchmarking

Both modern datacenter and embedded Field Programmable Gate Arrays (FPGAs) provide great opportunities for high-performance and high-energy-efficiency computing. With the growing public availability of FPGAs from major cloud service providers such as AWS, Alibaba, and Nimbix, as well as uniform hardware accelerator development tools (such as Xilinx Vitis and Intel oneAPI) for software programmers, hardware and software developers can now easily access FPGA platforms. However, it is nontrivial to develop efficient FPGA accelerators, especially for software programmers who use high-level synthesis (HLS). The major goal of this article is to figure out how to efficiently access the memory system of modern datacenter and embedded FPGAs in HLS-based accelerator designs. This is especially important for memory-bound applications; for example, a naive accelerator design only utilizes less than 5% of the available off-chip memory bandwidth. To achieve our goal, we first identify a comprehensive set of factors that affect the memory bandwidth, including (1) the clock frequency of the accelerator design, (2) the number of concurrent memory access ports, (3) the data width of each port, (4) the maximum burst access length for each port, and (5) the size of consecutive data accesses. Then, we carefully design a set of HLS-based microbenchmarks to quantitatively evaluate the performance of the memory systems of datacenter FPGAs (Xilinx Alveo U200 and U280) and embedded FPGA (Xilinx ZCU104) when changing those affecting factors, and we provide insights into efficient memory access in HLS-based accelerator designs. Comparing between the typically used soft and hardened memory systems, respectively, found on datacenter and embedded FPGAs, we further summarize their unique features and discuss the effective approaches to leverage these systems. To demonstrate the usefulness of our insights, we also conduct two case studies to accelerate the widely used K-nearest neighbors (KNN) and sparse matrix-vector multiplication (SpMV) algorithms on datacenter FPGAs with a soft (and thus more flexible) memory system. Compared to the baseline designs, optimized designs leveraging our insights achieve about \( 3.5\times \) and \( 8.5\times \) speedups for the KNN and SpMV accelerators. Our final optimized KNN and SpMV designs on a Xilinx Alveo U200 FPGA fully utilize its off-chip memory bandwidth, and achieve about \( 5.6\times \) and \( 3.4\times \) speedups over the 24-core CPU implementations.
Predictability and transferability of local biodiversity environment relationships

Biodiversity varies in space and time, and often in response to environmental heterogeneity. Indicators in the form of local biodiversity measures – such as species richness or abundance – are common tools to capture this variation. The rise of readily available remote sensing data has enabled the characterization of environmental heterogeneity in a globally robust and replicable manner. Based on the assumption that differences in biodiversity measures are generally related to differences in environmental heterogeneity, these data have enabled projections and extrapolations of biodiversity in space and time. However so far little work has been done on quantitatively evaluating if and how accurately local biodiversity measures can be predicted. Here I combine estimates of biodiversity measures from local biodiversity surveys with remotely-sensed data on environmental heterogeneity globally. I then determine through a cross-validation framework how accurately local biodiversity measures can be predicted within (“predictability”) and across similar (“transferability”)biodiversity surveys. I found that prediction errors can be substantial, with error magnitudes varying between different biodiversity measures, taxonomic groups, sampling techniques and types of environmental heterogeneity characterizations. And although errors associated with model predictability were in many cases relatively low, these results question - particular for transferability - our capability to accurately predict and project local biodiversity measures based on environmental heterogeneity. I make the case that future predictions should be evaluated based on their accuracy and inherent uncertainty, and ecological theories be tested against whether we are able to make accurate predictions from local biodiversity data.
Designing a Novel Monitoring Approach for the Effects of Space Travel on Astronauts’ Health

Space exploration and extraterrestrial civilization have fascinated humankind since the earliest days of human history. However, it was only until last century that humankind finally began taking significant steps towards these goals by sending astronauts into space, landing on the moon, and building the International Space Station. However, space voyage is very challenging and dangerous, and astronauts are under constant space radiation and microgravity. It has been shown that astronauts are at a high risk of developing a broad range of diseases/disorders. Thus, it is critical to develop a rapid and effective assay to monitor astronauts’ health in space. In this study, gene expression and correlation patterns were analyzed for 10 astronauts (8 male and 2 female) using the publicly available microarray dataset E-GEOD-74708. We identified 218 differentially expressed genes between In-flight and Pre-flight and noticed that space travel decreased genome regulation and gene correlations across the entire genome, as well as individual signaling pathways. Furthermore, we rationally designed a rapid assay of 32 genes which could be used to monitor astronauts’ health during space travel. Further studies, including microgravity experiments, are warranted to optimize and validate the proposed assay.
Phage Therapy Potentiates Second-Line Antibiotic Treatment against Pneumonic Plague

Plague pandemics and outbreaks have killed millions of people during the history of humankind. The disease, caused by Yersinia pestis bacteria, can currently be treated efficiently with antibiotics. However, in the case of multidrug-resistant (MDR) bacteria, alternative treatments are required. Bacteriophage (phage) therapy has shown efficient antibacterial activity in various experimental animal models and in human patients infected with different MDR pathogens. Herein, we evaluated the efficiency of ϕA1122 and PST phage therapy, alone or in combination with second-line antibiotics, using a well-established mouse model of pneumonic plague. Phage treatment significantly delayed mortality and limited bacterial proliferation in the lungs. However, the treatment did not prevent bacteremia, suggesting that phage efficiency may decrease in circulation. Indeed, in vitro phage proliferation assays indicated that blood has inhibitory effects on lytic activity, which may be the major cause of treatment inefficiency. Combining phage therapy and second-line ceftriaxone treatment, which are individually insufficient, provided protection that led to survival of all infected animals, presenting a synergistic protective effect that represents a proof of concept for efficient combinatorial therapy in an emergency event of a plague outbreak involving MDR Y. pestis strains. Author summary Plague, caused by Yersinia pestis bacteria, can be efficiently treated with antibiotics. However, alternative therapies for the case of natively evolved or maliciously generated antibiotic-resistant Y. pestis must be developed. Due to the global increase in antibiotic resistance, there is renewed interest in examining the effectiveness of bacteriophage-based alternative therapies. Here, using a mouse model of pneumonic plague, we demonstrate that phage treatment significantly delayed mortality. By monitoring bioluminescence of engineered Y. pestis strain and live bacterial counts, we show that phage therapy effectively inhibited bacterial proliferation in the lung but not in blood. In vitro analyses showed decreased phage activity in the presence of blood, which probably explains the low efficacy of phage treatment alone. Because combination therapies will be used in an emergency situation, we tested the efficacy of Y. pestis-lysing phages as adjunctive therapy with a second-line antibiotic, ceftriaxone. Whereas each individual treatment was insufficient, the combination provided effective protection and rescued all infected animals. These results clearly demonstrated the synergistic effect of combined phage and antibiotic therapy and represent a proof of concept for this alternative therapy against multidrug-resistant Y. pestis strains.
Performance Analysis of Water Quality Monitoring System in IoT Using Machine Learning Techniques

Nowadays, the Internet of things (IoT) and insights while sensing are being used in different areas of research to monitor, collect and separate knowledge from various sources of interest. As there is a huge increase in contamination of water from the industries in provincial to urban regions and over-use of land and ocean assets, the nature of water that each person can use has turned out to be more regrettable. High utilization of composts for crops and different synthetic concoctions in different segments like thermal and electrical power plants, leakages in the submarine pipeline, mining, and construction has significantly added to the general dip in water quality level as a whole. Water is the most significant need for human survival and thus there must be a framework made to test water characteristics and availability for drinking in urban communities and towns. The accessibility of good and better quality of water is most significant in counteracting outbreaks of waterborne diseases just as improving the quality of life. The point of the work proposed is to present a water quality observing system using machine learning techniques.
Averting and Mitigating the Effects of Uncertainties with Optimal Control in Industrial Networked Control System

Emerging trends, development and growth of industrial-networked control systems (i-NCSs) with real-time communication network drive it more susceptible to malicious intended intrusion and attack. Due to numerous advantages such as reduced maintenance, ease to install, ease of diagnosis and system wiring draws attention to use in various industrial and critical fields. The control performance and robust stability of NCS is directly related to reliable and successful transmission of critical information’s. So in this paper an approach is illustrated to alleviate the effects and avert the unwanted intended intrusive data through the designing of stability conditions and optimized control policies. An unwanted intrusion effects also forced us to design a controlled system, which is hard to be estimated by attackers, through the applications of optimization algorithms.
Depression Analysis of Social Media Activists Using the Gated Architecture Bi-LSTM

Twitter is one of the social media platforms that has evolved into an incredible environment for users to communicate with friends and other users to trade thoughts, videos, and photographs that reflect their present mood. Using social media allows academics to investigate people’ online data, revealing their moods and habits, and analyze their mental states. Since social media is becoming a part of our life, this study implies users’ moods by what they post in their tweets. This study will analyze Twitter data for depression sentiment using a verified public internet portal. Social information is presented as an important predictor of depression. This study suggests distinct deep learning classification strategies which can perform as a competent and adaptable strategy and have already been effectively utilized in Natural Language Processing jobs to explore the impact of depression diagnosis on Twitter. LSTM and Convolutional Neural Networks were used to analyze Twitter data for depression analysis. Bi-LSTM proven the highest accuracy of 95% for real time depression analysis. We also developed a depression analysis tool based on this study.
A Hierarchical Data Driven Method for Spatial Electric Load Forecasting

Spatial Load Forecasting (SLF) is the basis for distribution network planning. Most of the previous SLF methods, including empirical load density index method and the top-down simulation method, fail to give sufficient accurate forecasts on the quantity and distribution of the future load growth. Motivated by the increasing data sources and widely fulfilled land use planning in cities, a data driven approach for SLF is proposed in this paper. Firstly, a hierarchical bottom-up load accumulating approach is utilized based on clustered typical load profiles. Then land plots are described with multiple indices including intensity, development, climate and location, and the numerical spatial load densities of different land use types are obtained through a nonparametric kernel density estimation. Finally, a machine learning method is introduced to forecast the future plot load. The proposed data driven approach can greatly enhance the accuracy and operability of SLF for distributed network planning.
SupSLAM: A Robust Visual Inertial SLAM System Using SuperPoint for Unmanned Aerial Vehicles

Simultaneous localization and mapping (SLAM) is essential for unmanned aerial vehicle (UAV) applications since it allows the UAV to estimate not only its position and orientation but also the map of its working environment. We propose in this study a new SLAM system for UAVs named SupSLAM that works with a stereo camera and an inertial measurement unit (IMU). The system includes a front-end that provides an initial estimate of the UAV position and working environment and a back-end that compensates for the drift caused by the initial estimation. To improve the accuracy and robustness of the system, we use a new feature extraction method named SuperPoint, which includes a pretrained deep neural network to detect key points for estimation. This method is not only accurate in feature extraction but also efficient in computation so that it is relevant to implement on UAVs. We have conducted a number of experiments and comparisons to evaluate the performance of the proposed system. The results show that the system is feasible for UAV SLAM with the performance comparable to state-of-art methods in most datasets and better in some challenging conditions.
Unified Physical Threat Monitoring System Aided by Virtual Building Simulation

With increasing physical threats in recent years targeted at critical infrastructures, it is crucial to establish a reliable threat monitoring system integrating video surveillance and digital sensors based on cutting-edge technologies. A physical threat monitoring solution unifying the floorplan, cameras, and sensors for smart buildings has been set up in our study. Computer vision and deep learning models are used for video streams analysis. When a threat is detected by a rule engine based on the real-time analysis results combining with feedback from related digital sensors, an alert is sent to the Video Management System so that human operators can take further action.A physical threat monitoring system typically needs to address complex and even destructive incidents, such as fire, which is unrealistic to simulate in real life. Restrictions imposed during the Covid-19 pandemic and privacy concerns have added to the challenges. Our study utilises the Unreal Engine to simulate some typical suspicious and intrusion scenes with photorealistic qualities in the context of a virtual building. Add-on programs are implemented to transfer the video stream from virtual PTZ cameras to the Milestone Video Management System and enable users to control those cameras from the graphic client application. Virtual sensors such as fire alarms, temperature sensors and door access controls are implemented similarly, fulfilling the same programmatic VMS interface as real-life sensors. Thanks to this simulation system’s extensibility and repeatability, we have consolidated this unified physical threat monitoring system and verified its effectiveness and user-friendliness. Both the simulated Unreal scenes and the software add-ons developed during this study are highly modulated and thereby are ready for reuse in future projects in this area.
A Personalized Adaptive Algorithm for Sleep Quality Prediction using Physiological and Environmental Sensing Data

The lacking data from wearable sensors to solve different problems in the healthcare area is obvious since it is not easy to find enough volunteers to collect data. Moreover, human reacts very differently to medical treatment/ exercise levels/ stress and so on. Therefore, we need an advanced prediction model which can reuse the public data and can be adapt to personal data to predict health parameters. This paper introduces a solution for this issue. We present a novel personalized adaptive algorithm based on ensemble learning to predict sleeping efficiency, the proposed framework can be extended to solve many problems in healthcare applications. In this work, the global model is built based on ensemble learning with common features from all clients. The global model is then combined with the model from the client with more personalized features. The client model will learn and be updated model every day. Our proposed framework was tested in two data sets PMData and another private data set and showed better results than the conventional method. The proposed algorithm/ framework is a great step to solve the prediction problem in healthcare since each person has their own characteristics, responds differently to treatments/environment/stressful levels. The proposed algorithm is a big enhancement in building a health navigator system to enhance human health.
Modified geometrical highlight model of echoes from nonrigid surface sonar target

Based on the modified Kirchhoff approximation, a modified geometrical highlight model of echoes from nonrigid surface sonar target with four parameters, including the amplitude, the time delay, the phase jump and the local plane wave reflection coefficient, is presented in this paper. The target strengths of rigid, naked and water filled,and silent tiles coated truncated elliptic cone shell are calculated using the former and the modified geometrical highlight models. The results show that the former geometrical highlight model with three parameters is approximation of one order for naked shells at high frequencies. For the actual shells, the modified geometrical highlight model with four parameters should be used whether it is coated or not with silent tiles.
STIFFNESS ANALYSIS OF THE MATCHING OF LENGTHENED SHRINK-FIT HOLDER AND CUTTING TOOL IN HIGH SPEED MILLING

A finite element model of the matching of lengthened shrink-fit holder and cutting tool is established and an available approach to determine the reasonable contact rigidity coefficient is obtained.Subsequently,the influence of different interference,grip length and spindle speed on the grip rigidity of the matching of lengthened shrink-fit holder and cutting tool.Finally,the finite element model is verified based on the construction of measurement method of the grip rigidity.The results show that a reasonable contact rigidity coefficient is a key to create a correct finite element model of the matching of lengthened shrink-fit holder and cutting tool;there is a reasonable interference and grip length between the lengthened shrink-fit holder and cutting tool so that to have a steepless grip and have a good radial grip rigidity and at the same time to avoid the strength of lengthened shrink-fit holder to exceed it's yield limit which will reduce the precision and service life of lengthened shrink-fit holder;when spindle speed reach a extension the weakening influence of the centrifugal force on the radial grip rigidity of the matching of lengthened shrink-fit holder and cutting tool should been taken into account.
Person re-identification algorithm based on multi-module convolutional neural network

For the cross-border tracking needs of target persons in real complex scenes, a person re-identification algorithm based on a multi-module convolution neural network is proposed to solve the problem of person search and matching caused by person scale change, light change, posture change and other factors in the real environment. The algorithm takes ResNet50 as the backbone network of feature extraction. The STN network module is embedded into the backbone network to overcome the impact of person scale change. The IBN network module is integrated for person image color correction to compensate for the influence of illumination change in the real scene. And A person multi-branch feature extraction module is designed to effectively reduce the impact of person posture changes. Through person image feature expression and measurement learning calculation, person similarity matching across cameras is realized. Experiments show that this method has good performance in real complex scene test data, and its Rank-1 and mAP are 98.30% and 95.78% respectively. It can be used for person matching and search in a real complex environment, and has certain practical value.
Classification of Attention Deficit Hyperactivity Disorder using Machine Learning

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects a person’s brain activity. Many individuals are suffering from it unknowingly which affects their daily lives. With the help of Machine Learning, our goal is to design a tool that will help diagnose ADHD easier. The main conclusion of the tool is to provide a clear understanding of the symptoms that one might be experiencing. The tool uses an appropriately chosen Machine Learning algorithm to help in more accurate diagnosis, which includes the stage of ADHD being presented. The chosen Machine Learning Algorithm, Naive Bayes, has shown an accuracy of 82.5, Precision of 91 and Sensitivity of 91.
Deep Learning for Malware Classification Platform using Windows API Call Sequence

Malware attacks and the growth of new types of malwares are things for government and industry departments to consider. More and more types of malware attacks require preventative measures using deep learning for malware analysis to minimize the impact of malware attacks. In this case, the task of the cyber-attack detection team of the National Cybersecurity and Encryption Agency Threat Detection Agency is to perform malware analysis. This research implemented malware detection and classification using a deep learning model by leveraging a sequence of API calls. The learning model is built with two different recurrent neural network architectures, LSTM and GRU for comparison. The architecture comparison shows that LSTM is better than GRU. The test results show that the accuracy rates of the learning model using the LSTM architecture in binary classification and multiple class classification are 97.3% and 56.05%, respectively. In this study, we aim to build classification platform to classify malware using the classification model that has been made and enhancing the dataset by merging and update new data. The classification model testing result shown that 146 samples were correctly predicted, with an accuracy rate of 96.8%