Miracle-dz/UniFAR-FTUs huggingface dataset:
SM-YOLO: A Model for Real-Time Smoke Detection

To address the lack of up-to-date smoke detection datasets, we have compiled and labeled a variety smoke detection dataset called SM-dataset. This dataset contains a total of 11596 smoke images from natural scenes. Meanwhile, we introduce a new version of YOLO with better performance, which we call SM-YOLO. SM-YOLO builds on the original model of YOLOv5m, reduces the original three outputs to two, streamlines the original network structure and improves the loss of the original network. Compared with YOLOv5m, SM-YOLO has only 75% of the trainable parameters, but improves mAP@. 5 by relative 2%, and reduces the inference time of a single frame from 7.3 ms to 6.6 ms, which effectively improves the speed of smoke detection.
Vessel Pattern Recognition Using Trajectory Shape Feature

In the era of big data, analyzing vessels patterns using massive trajectory data has become the main method of mining activity pattern. Trajectory shape feature, as one of the important features of vessel trajectory data, can be used to identify the vessel activity patterns. But most of research only focused on the features such as standard deviation of latitude and longitude, navigation heading to the analysis of vessels trajectories. Therefore, considering the spatial-temporal feature of vessels data, we propose a method based on Sevcik fractal dimension to extract shape feature for identifying vessels activity types. Firstly, we segment the vessel trajectories to form the sub-trajectory according to the speed and temporal threshold. Secondly, we construct the feature vector of trajectory shape using the improved Sevcik fractal dimension algorithm. Then, we select the standard deviation of latitude and longitude and shape features extracted by Sevcik fractal dimension as the comparison features, and observe the performance in K-means and GMM algorithms respectively to verify the effectiveness of shape feature vectors we proposed. Finally, we select the simulation data and two real data sets for experimental analysis. The results show that the shape feature extraction algorithm can extract the shape features of trajectories, and the performance in classification algorithm is better than the standard deviation and Sevcik fractal dimension. So the method we proposed can realize the pattern recognition of vessel and abnormal trajectory analysist.
Impact of Feature Extraction and Feature Selection Algorithms on Punjabi Speech Emotion Recognition Using Convolutional Neural Network

As a challenge to refine the spontaneity and productivity of a machine and human coherence, speech emotion recognition has been an overriding area of research. The trustability and fulfillment of emotion recognition are largely involved with the feature extraction and selection processes. An important role is played in exploring and distinguishing audio content during the feature extraction phase. Also, the features that have been extracted should be resilient to a number of disturbances and reliable enough for an adequate classification system. This article focuses on three main components of a Speech Emotion Recognition (SER) process. The first one is the optimal feature extraction method for a Punjabi SER system. The second one is the use of an appropriate feature selection method that selects effectual features from the ones extracted in the first step and removes the redundant features to improve the conduct of emotion recognition. The third one is the classification model that has been used further for emotion recognition. So the scope of this article is to explain the three main steps of the Punjabi SER system: feature extraction, feature selection, and emotion recognition with classifier. The results have been calculated and compared for number of feature set combinations, with and without a feature selection process. A total of 10 experiments are carried out, and various performance metrics such as precision, recall, F1-score, accuracy, and so on, are used to demonstrate the results.
The Research of Predicting Student's Academic Performance Based on Educational Data

In recent years, with the continuous improvement of teaching informatization, online teaching or online and offline hybrid teaching has become the new normal of teaching in some schools. However, the biggest problem in online teaching is that it is difficult to predict students' academic performance. Therefore, it is necessary to design an effective method to predict students' academic performance more accurately. In this paper, a student academic level prediction method based on stacking model fusion is proposed. Logistic regression, random forest, XGBoost, and Naive Bayes are selected as base learners according to optimal fusion criteria and model properties. Furthermore, the structure and distribution of the features of the dataset are optimized by data preprocessing, feature coding, and feature selection, and the upper limit of the model expression is effectively raised. On this basis, according to the features of dataset and model performance, we select the appropriate model for model fusion, and further improve the prediction effect. Experiments are conducted on OULAD and xAPI datasets, and the results show that the prediction accuracy of the proposed method is better than that of traditional prediction methods. Finally, we analyze the factors that affect academic performance and give some specific suggestions.
The Portfolio Model Based on Temporal Convolution Networks and the Empirical Research on Chinese Stock Market

Aiming at determining the weights of selected investment targets to obtain higher and more stable investment returns, a Temporal Convolution Networks (TCN) based portfolio model, namely, TCN-portfolio model is proposed. TCN-portfolio model combines TCN-based time series processing and MLP-based cross-sectional data processing, and finally outputs the investment target weights which changes every ten trading days. We optimize the TCN-portfolio model using a Multi-Objective Genetic Algorithm (MOGA) which optimizes the rate of return and variance at the same time. The component stocks of Shanghai Securities Composite 50 index (SSEC 50) are selected as the investment targets. Experimental results on the test sets reveal that TCN-portfolio model performs well. Its average daily return rate is obviously greater than those of SSEC and SSEC 50, and the cumulative return rate of TCN-portfolio model is always greater than those of SSEC and SSEC 50 on the test data set.
A Novel 5.8 GHz Circularly Polarized Rectenna

This paper presents a novel circularly polarized rectenna designed and measured at 5.8 GHz for application involving microwave power transmission(MPT). A square patch microstrip antenna with truncated corners used for the rectenna is developed.The axial ratio achieved is 1.2 dB at 5.8 GHz.A silicon Schottky barrier diode is employed as the rectifying device.Measured conversion efficiency results of the rectenna prove that the misalignment between transmitting antenna and this circularly polarized rectenna dose not affect the performance of this rectenna,which is suitable to be used in mobile application of MPT system.
Study and Analysis of the Collaborative Approach of Artificial Intelligence and Medical in Effective Healthcare: A Systematic Review

Artificial Intelligence (AI) has been making tremendous progress in transforming industries across different sectors globally, and healthcare is no exception. Increasing cost and patient outcomes are two recurring challenges faced by the healthcare industry. AI is very useful to manage the health insurance claims. Some other areas where data and information collected by AI engines are very useful to identify the suitable health support, services and consultancies for patients. It is also useful for pharmaceutical department as for drug and vaccine development. Artificial Intelligence is one of the important field of Computer Science and Engineering which plays a vital role to shape various applications to make the life better for human beings. This application can be from the field of sales, business, marketing, healthcare and other field to change the life of the human being. In the field of medical and health care, there is challenge of human resources as the doctors, nurses and other medical staff. This challenge can be handled by the Artificial Intelligence through various application to handle this challenge in the field of Health care. Various fields and application of AI like image processing, Natural Language Processing, Machine Learning, etc. over structured and unstructured data sets. This paper highlights the statics on the analysis of the available documents related to AI in healthcare.
A Comprehensive Review on Table Driven Routing Strategies in MANET

Dynamic topology imposes challenges in designing an efficient routing protocol, for enabling successful communication between mobile devices in MANET. Based upon nature of working, routing protocols are broadly classified into proactive and reactive protocols. This work is focused on the review of work done by many researchers in the direction of improving the performance of table-driven routing strategies viz., Destination Sequenced Distance Vector (DSDV) and Optimized Link State Routing (OLSR) protocol. Specifically, the efforts made by various researchers to improve the conventional MPR selection strategy of OLSR have been explored, to identify the context for new research. Furthermore, review on energy considerations of mobile nodes in OLSR to improve its performance, has helped in understanding the future scope. This paper provides insights to researchers working in the area to further explore the new research directions.
18nm FDSOI Technology Platform embedding PCM & Innovative Continuous-Active Construct Enhancing Performance for Leading-Edge MCU Applications

For the first time in the industry, 18nm FDSOI technology platform meeting the performance target for next MCU is presented based on triple gate oxide and HKMG process. To meet the performance target of next MCU, continuous-active and device width increase by “contact on gate over active” with wide process margin are utilized. Good yield in 64Mb SRAM array for all SRAM bitcells (bitcell area 0.102um2 and 0.124um2 for ultra low leakage and high speed, respectively) including ultra low leakage bitcell with retention leakage under 1pA is confirmed. 500hrs HTOL for all SRAM bitcells and WLR for all SG/EG/eZG in TGO are passed. ePCM (embedded Phase Change Memory) is co-integrated in 18nm FDSOI process as non-volatile memory option.
Formal Verification of HPS-based Master-Slave Scheme in MEC with Timed Automata

The combination of D2D (Device to Device) communication and MEC (Mobile Edge Computing) architecture is adopted to reduce the communication delay in 5G. With the extensive use of HPS (high performance switch) as the main medium of data distribution in the device layer, the holistic power consumption and delay can be guaranteed stably, mainly because HPS has the mechanism of Master-Slave scheme to ensure the robustness. However, as far as we know, there are fewer studies to verify the safety validation of the design using formal methods. Hence, we study it through timed automata and model checking. This paper integrates HPS into D2D communication design in MEC, selects the Master-Slave synchronization and switchover scheme in HPS as the research object, describes the scheme based on timed automata, and then verifies seven fundamental but essential properties through the model checker UPPAAL. Through verification, comparison and analysis of possible scenarios in our constructed model, it can be concluded that the design of the HPS in MEC can conform to its required specification. Further, it can not only enhance the safety validation of the combination of D2D communication and MEC architecture, but also provide a guide for the newest subsequent high safety primary-backup design in industry.
Transportation: What's Behind the Air-Taxi Craze: A Wave of eVTOL Startups Aim to Revolutionize Transportation

When entrepreneur JoeBen Bevirt launched Joby Aviation 12 years ago, it was just one of a slew of offbeat tech projects at his Sproutwerx ranch in the Santa Cruz mountains, in California. Today, Joby has more than 1,000 employees and close to US $2 billion in funding.
Complementary Context-Enhanced Concept Lattice Aware Personalized Recommendation

With the rapid development of information technology, the huge amount of information is booming exponentially, and it becomes a key challenge to find the information users required from the massive information. To tackle this challenge, this paper focuses on developing a novel personalized recommendation approach based on complementary context-enhanced concept lattice. To be specific, the proposed approach constructs the concept lattices for both formal context and complementary context of user-item interactive data, and then adopts a Formal Concept Analysis based association rule recommendation algorithm for obtaining two recommendation result sets separately, and ultimately analyzes the acquisition of the final resulting recommendation for each of these two recommendation result sets under different situations. Taking movie recommendation as an illustrative example, more accurate user-preferred movie recommendation can be achieved. Compared with the traditional personalized recommendation algorithms, it is confirmed that our recommendation approach is more reasonable and effective in the real recommendation systems.
Emergency Alert System for Neonatal Unit using IoT

As normal new-born babies, premature new-born babies cannot control their own body temperature. By having an atmosphere that can be changed to provide the optimum temperature as well as the right amount of oxygen, humidity, and light, neonatal incubators play a crucial role in saving premature babies' lives. Owing to a lack of adequate control of the incubator, some premature babies have lost their lives. If an unexpected condition occurs, via an alarm system, it will be signaled to the health professional's mobile device. Early treatment will be provided to the infected babies by alerting them and the chance of recovery will be increased. We concentrated on monitoring the biological parameters for multiple incubators to resolve this problem. And if there are any fluctuations in the biological parameters from the usual standard to be preserved, then the health professionals will be indicated through the blynk application.
Moonlighting proteins activate transformation in multidrug-resistant Streptococcus 1 pneumoniae epigenetic phase variants

Transformation of Streptococcus pneumoniae GPSC1 has enabled vaccine evasion and the 16 acquisition of antibiotic resistance. Epigenetic phase variants of GPSC1 isolate RMV7, 17 resulting from rearrangements at the tvr restriction-modification locus, differed ~100-fold in 18 their transformation efficiency. This variation was recapitulated in knock-in mutants of the 19 relevant tvr alleles. RNA-seq showed the difference was due to blocking of the early 20 competence regulatory cascade. The more transformable variant more highly expressed 21 manLMN , encoding a carbon source importer. This was shown to be necessary for efficient 22 competence induction, despite being dispensable for growth in rich media. Transformation 23 was promoted by import of N -acetylglucosamine, which activated competence through an 24 orthologue of the gram-negative competence regulator TfoX, and an enzyme likely involved 25 in nucleotide-mediated signalling. The less transformable variant more highly expressed 26 mobile element genes, causing a protein misfolding stress response mediated by HrcA. This 27 chaperone regulator caused the decrease in transformation efficiency associated with heat 28 shock, and the increase associated with elevated Ca 2+ concentrations. Hence both ManLMN 29 and HrcA moonlighted as activators of competence. Such proteins, conserved by selection 30 on their primary function, are five
First Experimental Demonstration of MRAM Data Scrubbing: 80 Mb MRAM with 40 nm junctions for Last Level Cache Applications

This work provides the first experimental demonstration of data scrubbing effectiveness for MRAM, using an 80 Mb array with 40 nm critical dimension (CD). We demonstrate improved endurance without chip error rate (CER) penalty for last level cache (LLC) applications. An analytical model is built to understand the 10 year chip error rate dependence on data scrubbing frequency, error correction code (ECC) strength, and data retention energy barrier (Eb). A low Eb MTJ stack is then compared to the process of record (POR) stack for data scrubbing at 85 C, showing 20% Eb and 15% switching current reduction without degrading CER with 5 s scrubbing. Finally, an ultra-low Eb stack is explored, delivering 30% Eb reduction and 2 orders of magnitude higher endurance without CER penalty by using 1 s scrubbing. This work points out the great potential of MRAM for LLC applications with data scrubbing.
Distinct functions of three chromatin remodelers in activator binding and preinitiation complex assembly

The nucleosome remodeling complexes (CRs) SWI/SNF, RSC, and Ino80C cooperate in evicting or repositioning nucleosomes to produce nucleosome depleted regions (NDRs) at the promoters of many yeast genes induced by amino acid starvation. We analyzed mutants depleted of the catalytic subunits of these CRs for binding of transcriptional activator Gcn4 and recruitment of TATA-binding protein (TBP) during preinitiation complex (PIC) assembly. RSC and Ino80 were found to enhance Gcn4 binding to both UAS elements in NDRs upstream of promoters and to unconventional binding sites within nucleosome- occupied coding sequences; and SWI/SNF contributes to UAS binding when RSC is depleted. All three CRs are actively recruited by Gcn4 to most UAS elements and appear to enhance Gcn4 binding by reducing nucleosome occupancies at the binding motifs, indicating a positive regulatory loop. SWI/SNF acts unexpectedly in WT cells to prevent excessive Gcn4 binding at many UAS elements, indicating a dual mode of action that is modulated by the presence of RSC. RSC and SWI/SNF collaborate to enhance TBP recruitment at Gcn4 target genes, together with Ino80C, in a manner associated with nucleosome eviction at the TBP binding sites. Cooperation among the CRs in TBP recruitment is also evident at the highly transcribed ribosomal protein genes, while RSC and Ino80C act more broadly than SWI/SNF at the majority of other constitutively expressed genes to stimulate this step in PIC assembly. Our findings indicate a complex interplay among the CRs in evicting promoter nucleosomes to regulate activator binding and stimulate PIC assembly. AUTHOR SUMMARY: ATP-dependent chromatin remodelers (CRs), including SWI/SNF and RSC in budding yeast, are thought to stimulate transcription by repositioning or evicting promoter nucleosomes, and we recently implicated the CR Ino80C in this process as well. The relative importance of these CRs in stimulating activator binding and recruitment of TATA-binding protein (TBP) to promoters is incompletely understood. Examining mutants depleted of the catalytic subunits of these CRs, we determined that RSC and Ino80C stimulate binding of transcription factor Gcn4 to nucleosome-depleted regions, or linkers between genic nucleosomes, at multiple target genes activated by Gcn4 in amino acid-starved cells, frequently via evicting nucleosomes from the Gcn4 binding motifs. At some genes, SWI/SNF functionally complements RSC, while opposing RSC at others to limit Gcn4 binding. The CRs in turn are recruited by Gcn4 to establish positive or negative feedback loops that control Gcn4 binding. The three CRs also cooperate to enhance TBP recruitment, again involving nucleosome depletion, at both Gcn4 target and highly expressed ribosomal protein genes, whereas only RSC and Ino80C act broadly throughout the genome to enhance this key step in preinitiation complex assembly. Our findings illuminate functional cooperation among multiple CRs in regulating activator binding and promoter activation.
High-throughput analysis of dendritic and axonal arbors reveals transcriptomic correlates of neuroanatomy

Neuronal anatomy is central to the organization and function of brain cell types. However, anatomical variability within apparently homogeneous populations of cells can obscure such insights. Here, we report large-scale automation of neuronal morphology reconstruction and analysis on a dataset of 813 inhibitory neurons characterized using the Patch-seq method, which enables measurement of multiple properties from individual neurons, including local morphology and transcriptional signature. We demonstrate that these automated reconstructions can be used in the same manner as manual reconstructions to understand the relationship between some, but not all, cellular properties used to define cell types. We uncover gene expression correlates of laminar innervation on multiple transcriptomically defined neuronal subclasses and types. In particular, our results reveal correlates of the variability in Layer 1 (L1) axonal innervation in a transcriptomically defined subpopulation of Martinotti cells in the adult mouse neocortex.
High Performance 4nm FinFET Platform (4LPE) with Novel Advanced Transistor Level DTCO for Dual-CPP/HP-HD Standard Cells

In this paper, a leading edge High performance 4nm FinFET Platform (4LPE) is demonstrated, featuring a novel advanced Transistor level DTCO (T-DTCO) in Middle-of-Line/Back-End-of-Line and careful process optimization, which enables dual-CPP/HP-HD standard cell with Gate contact-over-RX scheme. Compared to 5nm Platform (5LPE), Performance +7~10% and Power -12% gain are successfully obtained in both typical and low Vdd, as well as a flat layout dependence. 4LPE enhancement is accomplished by design-manufacturing favorable approaches. Moreover, newly adapted SRAM Ultra High-Density cell (UHD), Product IDDQ variation control and reliability verifications were completed for the technology delivery.
Camera based Light Emitter Localization using Correlation of Optical Pilot Sequences

Visual identification of objects using cameras requires precise detection, localization, and recognition of the objects in the field-of-view. The visual identification problem is very challenging when the objects look identical and features between distinct objects are indistinguishable, even with state-of-the-art computer vision techniques. The problem becomes significantly more challenging when the objects themselves do not carry rich geometric and photometric features,for example, in visual identification and tracking of light emitting diodes (LED) for visible light communication (VLC) applications. In this paper, we present a camera based visual identification solution where objects or regions of interest are tagged with an actively transmitting LED. Motivated by the concept of pilot symbols, typically used for synchronization and channel estimation in radio communication systems, the LED actively transmits unique pilot symbols which are detected by the camera across a series of image frames using our proposed spatio-temporal correlation based algorithm. We setup the visual identification as a problem of localization of the LED on the camera image, which involves identifying the (pixels) and the unique ID corresponding to the LED. In this paper, we present the algorithm and trace-based evaluation of the identification accuracy under real-world conditions including indoor, outdoor, static and mobile scenarios. In addition to micro-benchmarking the localization accuracy of our technique across different parameter configurations, we show that our technique outperforms comparative techniques, including, color based detection, Support-Vector Machine based (SVM) machine learning, and You Only Look Once (YOLO), which is a state-of-the-art Convolutional Neural Network (CNN) deep learning based object identification tool.