Miracle-dz/UniFAR-FTUs huggingface dataset:
A Real-Time Deep Transfer Learning Model for Facial Mask Detection

With the rise of COVID-19, wearing masks in public areas has become paramount to preventing the transmission of the coronavirus, Furthermore, many public service providers may require customers to properly wear masks in order to be served. But current enforcement measures of mask mandates are mostly monitored by humans, and thus may be hard to execute effectively in densely populated and fast-moving venues like airports and public transit. An automated mask detection monitoring system could greatly reduce the required human labor. In this study, a facial mask detection software model, usable in existing surveillance applications such as airport monitoring systems, is developed using Keras, a high-level deep learning API, and TensorFlow, an end-to-end open source platform for machine learning. The model uses deep convolutional neural networks (DCNN), which were trained from a database of roughly 12,000 images of faces, with roughly a 50-50 split of masked and unmasked, with the goal of achieving binary classification to detect whether a person is wearing a mask. The DCNN mask detection software achieved a validation accuracy of 98 percent. We also used computer vision techniques from OpenCV including facial detection and contours to detect the location of the people’s faces. The DCNN was then paired with this application to achieve live feed object detection by first detecting a person, and then determine whether they are wearing a mask. Finally, we explore the potential implementation and applications of the facial detection model via the linkage of cameras in monitoring areas to signal and notify officials.As more countries are developing mask wearing regulations, automated masked face detection is a key real-world application [3]. Transportation systems are one area that have been considerably affected by the pandemic, as cities around the world had to enforce massive restrictions on public transport in order to limit transmission of the virus and ensure the safe passage of key workers during the emergency response [4]. Masks have since been required on planes, buses, trains, and other forms of public transportation traveling into, transportation hubs such as airports and stations [5]. However, Machine Learning and Deep Learning can help to combat Covid-19 in many ways. Enabling researchers and clinicians to evaluate a vast amount of data to make various predictions. In particular, face mask detection may prove to be useful as a guide for government surveillance on people in public areas susceptible to transmission, to ensure that face mask regulations are upheld. To achieve this, deep learning binary classification facial mask model based on VGG19 and one-shot object detection methods will be trained.We contribute a dual face-and-mask detection system that can provide immediate feedback in live surveillance systems. The method introduced is fast and accurate and can work for live mask-checking in busy public areas, alleviating some of the human effort and error typically subject to the mask mandating task.
Estimation of Joint Kinematics and Fingertip Forces using Motoneuron Firing Activities: A Preliminary Report

A loss of individuated finger movement affects critical aspects of daily activities. There is a need to develop neural-machine interface techniques that can continuously decode single finger movements. In this preliminary study, we evaluated a novel decoding method that used finger-specific motoneuron firing frequency to estimate joint kinematics and fingertip forces. High-density electromyogram (EMG) signals were obtained during which index or middle fingers produced either dynamic flexion movements or isometric flexion forces. A source separation method was used to extract motor unit (MU) firing activities from a single trial. A separate validation trial was used to only retain the MUs associated with a particular finger. The finger-specific MU firing activities were then used to estimate individual finger joint angles and isometric forces in a third trial using a regression method. Our results showed that the MU firing based approach led to smaller prediction errors for both joint angles and forces compared with the conventional EMG amplitude based method. The outcomes can help develop intuitive neural-machine interface techniques that allow continuous single-finger level control of robotic hands. In addition, the previously obtained MU separation information was applied directly to new data, and it is therefore possible to enable online extraction of MU firing activities for real-time neural-machine interactions.
Research on knowledge graph for quantification of relationship between enterprises and recognition of potential risks

It is always a complex and important research problem to find the correlation between enterprises and identify the potential risks of enterprises and grey list. In this paper, we adopted a method of knowledge graph building for graph neural network, including the quantification of association information among enterprises by constructing the knowledge graph of relations between enterprises. Based on the establishment of the knowledge graph among enterprises, we use the relationship algorithm to identify the potential risks of the target enterprises by taking the target enterprises as the starting point to find the associated enterprises in the knowledge graph.
Chemical and Nutritional Changes in Sunflower Silage Associated With Molasses, Lactic Acid Bacteria and Enzyme Supplementation

This study was conducted to determine the effects of different silage additives on sunflower silage quality. The treatments were as follows: (1) control (C, no additive), (2) 5% molasses (M) (3) inoculation of lactic acid bacteria (LAB; 1.5 g/ton, a mixture of Lactobacillus plantarum and Enterococcus faecium applied at a rate of 6.00 log10 cfu LAB/g of fresh material) and (4) LAB+enzyme mixture 2 g/ton (LEN, Lactobacillus plantarum bacterium (6.00 log10 cfu/g) and cellulase (150000 CMCU/kg) and amylase (200000 SKB/kg) enzymes). Silage additives were mixed and stored in glass jars with 5 replicates for 90 days. There were no significant differences among the groups in terms of pH, ether extract (EE), acid detergent lignin (ADL) and hemicellulose (HEM). The water soluble carbohydrate (WSC) concentration was higher in the M group than those of other groups (P<0.01). Lactic and acetic acid concentration were lower in the LEN group than the other groups (P<0.05). In the LAB group, dry matter (DM), crude protein (CP), crude ash (CA) and fleig point (FP) contents were lower than those of other groups (P<0.01). In the M group, neutral detergent fiber (NDF) and acid detergent fiber (ADF) contents were lower and the total digestibility nutrient (TDN) and non-fiber carbohydrate (NFC) contents were higher than those of other groups (P<0.01). The crude cellulose (CC) contents of M and LEN groups were lower than that of C and LAB groups. In conclusion, addition of the bacterial inoculants (LAB and LEN) and molasses to sunflower crop before ensiling positively affected some quality traits.
Recursive PID controller for automatically adjusting M-wave size during H-reflex operant conditioning

This paper presents a recursive proportional-integral-derivative (rPID) controller for automatically adjusting M-wave size during H-reflex operant conditioning. H-reflex operant conditioning is a recently developed rehabilitation method for the recovery of movement following spinal cord injury and other neuromuscular disorders. During this protocol, researchers and clinicians manually control the M-wave size-an electromyographic (EMG) indicator of electrical stimulation intensity. Here, we compare the present method for manually controlling M-wave size to two versions of the rPID controller ($\text{rPID}_{\beta= 0.45}$ and $\text{rPID}_{\beta=0.95}$) during H-reflex operant conditioning trials of the flexor carpi radialis. Initial experiments with three participants show that the manual controller and the $\text{rPID}_{\beta=0.95}$ controller successfully maintained M-wave size with similar success rates, mean squared errors, and number of trials before the first trial within ±20% of the target M-wave size. Our new automated system—with further testing and optimization—will allow researchers and clinicians to focus more on participants, reducing the amount of training they require to perform H-reflex operant conditioning, and potentially improving the efficiency and efficacy of H-reflex operant conditioning protocols.
Iterative Boundaries Implicit Identification for Superpixels Segmentation: A Real-Time Approach

Superpixel algorithms group visually coherent pixels and form an alternative representation of the regular structure of the pixel grid. This fundamental low-level computer vision preprocessing step greatly reduces the complexity of subsequent image processing tasks. However, most of the existing methods suffer from very high calculation costs which makes them quite unsuitable for time-sensitive applications. In this paper, we propose a new superpixel segmentation method, named IBIS for Iterative Boundaries implicit Identification for superpixels segmentation, that implicitly identifies the boundaries between superpixels and performs the segmentation using only a fraction of the pixels of the input image, thereby reducing the complexity and computation time. The results obtained during the experiments show that the segmentation quality of IBIS is comparable to that of state of the art methods with a computation time divided by a factor of 8 without parallelization of the processing for low resolution images (e.g., $320\times 240$ pixels) as usually provided in public data sets. We also present and comprehensively evaluate the GPU variant of IBIS named IBIScuda that allows an optimal exploitation of the available resources considering the limited bandwidth between CPU and GPU memories.
ECG Guided Automated Diagnostic Intervention of Cardiac Arrhythmias with Extra-Cardiac Noise Detection using Neural Network

The electrocardiogram's (ECG) cyclic activity provides information about a person's emotional, behavioural, and cardiovascular health. Noise that occurs during acquisition and symptomatic patterns produced by pathologies are two examples of irregular behaviours that affect the analysis of these signals. This paper presents a Deep Neural Network algorithm that learns the normal behaviour of an ECG when identifying irregular events, which is studied in two different settings: noise detection and symptomatic events triggered by multiple pathologies. Two noise detection algorithms were developed using an auto-encoder and Convolution Neural Networks (CNN), with the binary class model achieving 98.18 percent accuracy and the multi-class model achieving 70.74 percent accuracy in distinguishing between base wandering, muscle artefact, and electrode motion noise. Recurrent Neural Networks and an autoencoder with Gated Recurrent Units (GRU) configuration were used to create the arrhythmia detection algorithm. With a 56.85 percent accuracy and a 61.13 percent overall sensitivity for a 7-class model. It was determined that the machine's learning mechanism learned characteristics of a regular ECG signal, sacrificing precision for greater generalisation at the moment. In the ECG, the frequency of sporadic events is more discriminatory than the classification of different types of events.
Correlated inputs to striatal population drive subthalamic nucleus hyper-synchronization

Movement disorders such as Parkinson's disease (PD) are often associated with hyper-synchronization in basal ganglia (BG) activity. The origins of the hyper-synchronization and its interaction with deep brain stimulations (DBS) of BG nuclei are still debated. The structure of the BG suggests that strong neuronal correlations cannot arise within the BG but should rather be inherited from cortical or thalamic inputs. However, this is inconsistent with the hypothesized decorrelating role of the striatal network. Here we investigate to what extent striatum can decorrelate correlated inputs, and the consequences of correlations in striatal activity on the dynamics of the BG; especially, the beta-band oscillations, which are the cardinal symptom of PD. We developed a BG network model with spiking neurons and analyzed the effects on input correlation on BG activity. We found that striatum does not completely decorrelate the inputs and that the residual correlations can synchronize the whole BG. These results provide new insights into the origin of BG hyper-synchronization and pave the way to understand DBS induced disruption of synchrony and ensuing therapeutical effects.
Person Presence Detection and Control the Water Faucet Line in the Bathtub

A smart bathtub system is a new type of accommodation with modern and intelligent facilities of a smart home automation system. It is equipped with a multi-sensor system that will be used to recognize human activities within the bathtub to make it secure, save energy and water. This system design with an interaction design process aims to enlarge product usability; was applied during the development tools. In this smart bathtub system, sensors have been installed with a variety of appliances, the presence sensor can be used to monitor human occupations, also the water level sensor to read the water level in the bathtub. Another part is the auto water faucet line that is supposed to flow water in the bathtub. The presence sensors detect a presence in the bathtub that would automatically initiate the automatic process. First, it detects the presence, and it checks the human activity in the bathtub while the system is active. If the object is present in the bathtub then the automation system will work otherwise not, like water level sensors and water faucet lines would not work if a person’s presence is not in the bathtub. Water level sensors send reading data to the microcontroller, if the water is empty water faucet will turn on, and water flows in the tub. The results demonstrate that the proposed method can detect the conditions of the bathtub presence activity and water level detect effectively with the best-experimented results showed 100% that the water line could be open if presence in the bathtub.
Probabilistic Scheduling in High-Level Synthesis

High-level synthesis (HLS) tools automatically transform a high-level program, for example in C/C++, into a low- level hardware description. A key challenge in HLS tools is scheduling, i.e. determining the start time of all the operations in the untimed program. There are three approaches to scheduling: static, dynamic and hybrid. A major shortcoming of existing approaches to scheduling is that the tools either assume the worst- case timing behaviour, which can cause significant performance loss or area overhead, or use simulation-based approaches, which take a long time to explore enough program traces.In this paper, we propose a probabilistic model that allows HLS tools to efficiently explore the timing behaviour of hardware generated from all these scheduling approaches. We capture the performance of the hardware using Petri nets, allowing us to leverage off-the-shelf Petri net analysis tools to make HLS decisions.We demonstrate the utility of our approach by using it to automatically infer the optimal initiation interval (II) for statically scheduled components that form part of a larger dynamically scheduled circuit. An empirical evaluation on a range of benchmarks suggests that by using this approach, on average we incur a 2% overhead in area-delay product (ADP) compared to optimal designs. In contrast, the static analysis in Vitis HLS incurs a 112% ADP overhead, while the throughput analysis in the dynamically scheduled Dynamatic tool incurs a 17% ADP overhead.
SCADA-NLI: A Natural Language Query and Control Interface for Distributed Systems

Human–Computer natural language interaction is helpful to reduce the operation and maintenance cost of the SCADA system, and it is necessary to solve the complex natural language interface problem that supports data query and real-time control. According to the complexity of natural language instructions, a hierarchical classification of semantic parsing algorithm is adopted. Firstly, the KWECS method is used to classify the intent of natural language instruction, then the TF-IDF keyword extraction algorithm combined with the cosine similarity is used to structure the key-value of the classified natural language instructions which was used into SCADA control intermediate language and then formally converted into actual control or query instruction. If the analysis fails, the complex control and query instruction analysis are carried out according to the classification results, structuring instruction parsing based on dependency parsing and SQL natural language parsing based on deep learning are adopted respectively to implement real-time control interface and database query interface. Our experimental results show that the proposed hierarchical classification of natural language comprehensive query and control interface can better solve the problem of human-computer natural language interaction in the SCADA system, and the accuracy of intent recognition reaches 96.5%. In more detail, the accuracy, precision, recall, and F-score of instruction parsing reach 88.47%, 90.21%, 89.48%, and 89.72% respectively. Especially, it provides more convenient interactive means for industrial and agricultural information management and control.
Mood classification from Song Lyric using Machine Learning

Nowadays, many people change the way they listen to music by listening to the mood of the songs in the tracks. This research is interested in analyzing song extraction using natural language processing to acquire mood information. Lyrics are valuable for categorizing music. First, removing special characters and using Term-frequency/inverse-document frequency (TFIDF) and then Latent Dirichlet Allocation (LDA) are used to connect words to mood classes. We perform a lyric- based mood classification on local machine learning classifiers such as Random forest, Decision tree, Naïve Bayes, Logistic Regression, AdaBoost and XGBoost. Using grid search for tuning the best parameter yield the results XGBoost shows the highest accuracy. It can prove that boosting algorithms have better performance than local machine learning in this research.
The Importance of Being X-Drop: High Performance Genome Alignment on Reconfigurable Hardware

Pairwise sequence alignment accounts for the majority of key genome analysis applications’ runtime. Because of the quadratic time complexity of exact alignment algorithms, the community is moving away from exact algorithms in favor of heuristics that only compute high-quality results. However, the state of the art lacks hardware-accelerated versions of these heuristic algorithms as the vast majority of the available solutions still rely on implementing exact alignment algorithms. Moreover, hardware-based implementations lack high-level APIs that can simplify their integration in commonly used genomic pipelines, hindering their applicability in real-world scenarios. In this context, we present the first high-performance FPGA implementation of the popular X-drop heuristic alignment algorithm and provide an easy-to-use API for its integration. On a Xilinx Alveo U280, our FPGA design achieves up to 5× speed-up over SeqAn, the state-of-the-art software version of the algorithm, running on two Intel Xeon processors using 80 CPU threads. Moreover, our design is also 3.45× faster than ksw2, a state-of-the-art vectorized alignment algorithm that performs a similar heuristic to the one employed in the X-drop algorithm. Finally, our implementation also outperforms LOGAN, a recently published GPU implementation of X-drop running on an Nvidia Tesla V100, by a factor of 1.5×.
Design of EBG using Double Interdigital Coplanar Waveguide Structure for Reduced Size and High Gain Antenna

A new Mushroom-like Electromagnetic Band Gap (EBG) structure with double interdigital coplanar waveguide is proposed. The proposed structure has a fundamental frequency of 1.8 GHz and a size of λ/8. An array of 10×10 unit cells is designed to serve as a reflector to increase the gain of a dipole antenna. The antenna gain is increased to 7.98 dBi. The directivity of the antenna is increased to 7.82 dBi.
Automated Observability Analysis for Mixed-Signal Circuits

Recent papers have proposed methods for more efficient mixed-signal test generation, measuring test stimulus quality, and defect simulation. This paper addresses the most timeconsuming remaining step to achieving higher quality mixedsignal ICs: diagnosing defects reported as undetected by an analog fault simulator. A unique method for automatically reporting the observability of each undetected defect is described that works for analog, mixed-signal, and even digital circuits (that don’t have scan path access). Results are reported for an 8-bit SAR ADC from the ITC’17 A/MS benchmark circuits, and they reveal that most of the undetected defects can be quickly categorized into those that need a better test stimulus, those that need better output analysis, and those that might need better DFT.
Updating BCI paradigms: Why to design in terms of the user?

Towards the adaptation of Brain-Computer Interfaces (BCIs) from the laboratories to real contexts, the challenge of providing an efficient interaction becomes more notorious. Current paradigms are adapted to a technical perspective which suffices only a fraction of requirements to control a computational application with neural activity. For example, human aspects have been usually considered a secondary factor in non-invasive BCI research community, what has resulted in a poor user adaptation process and a system underperformance. This phenomenon has been named as “BCI-Illiteracy”. This concept is questioned since its inclusion in literature may have led to a strong bias in the outcome interpretation, pointing towards the user underperformance rather than the system inefficiency. This paper explores alternative designs proposed in terms of system architecture, experimental procedures and user-centered design as key factors to update current BCI paradigms towards a balance between user needs, and current technology. Several cues, results and findings proposed in recent research are presented as options to be considered for the design of new interactive paradigms, which could lead to an efficient application, and thus matching the expectations currently surrounding BCI systems.
Defect Detection of Rubber Gloves Based on Normal Samples

Rubber gloves are widely favored in various industries, and the defect detection link in production is very important. This paper proposes a method for detecting defects in rubber gloves based on normal samples (no defects). First, random noise is added to the collected rubber glove images, and the size is standardized and unified. Secondly, establish an improved GANomaly network model. According to the small and difficult defect to distinguish characteristics of black spots and oil defects of rubber gloves, a noise-reducing convolutional autoencoder is built in the network model as a generation network, and the least square loss is introduced for the model of confrontation training. The model only uses normal sample input for training, learning the characteristic distribution of the normal sample and performs image reconstruction, and judges whether it has defects according to the reconstruction effect score of the input sample during testing. The experiment is based on the rubber glove image data set, and the results show that the method can accurately identify whether there are defects on the rubber glove.