Reviewerly/exHarmony huggingface dataset:
Decompose Auto-Transformer Time Series Anomaly Detection for Network Management

Time series anomaly detection through unsupervised methods has been an active research area in recent years due to its enormous potential for networks management. The representation and reconstruction of time series have made extraordinary progress in existing works. However, time series is known to be complex in terms of their temporal dependency and stochasticity, which makes anomaly detection difficult. To this end, we propose a novel approach based on a decomposition auto-transformer networks(DATN) for time series anomaly detection. The time series is decomposed into seasonal and trend components, and renovated as a basic inner block deep model. With this design, transformers can decompose complex time series in a progressive manner. We also design an auto-transfomer block that determines dependencies and representation aggregation at the sub-series level based on series seasonal and trend components. Moreover, the complex transformer decoder is replaced by a simple linear decoder, which makes the model more efficient. Extensive experiments on various public benchmarks demonstrate that our method has achieved state-of-the-art performance.
A dynamically reconfigurable entropy source circuit for high-throughput true random number generator

—True random number generator is an important hardware security primitive. Entropy source is the key to the design of a high-throughput true random number generator. To improve the throughput of a true random number generator while maintaining low hardware overhead, a novel entropy source circuit using ring oscillator with dynamically reconfigurable delay chain is proposed in this paper. The proposed entropy source exploits both signal jitter and circuit metastability. It consists of a seventh-order cycle shift register, seven 2-1MUXs and a core loop delay chain that is composed of inverters in series. Under the control of a high-frequency clock, the cycle shift register generates state transitions to control the output of MUXs, which in turn changes the number of inverters connected in the delay chain, switching the operation of the ring between steady and oscillating states. The outputs of five entropy sources are sampled and further XOR-ed with D flip-flops at a sampling clock of 400 MHz to obtain true random numbers. The proposed TRNG is implemented on the Xilinx Virtex-6 FPGA. The random numbers are captured in real-time using the Xilinx ChipScope and the TRNG is tested using the standard test specifications. The experimental results show that when working under a throughput of 400 Mbps, the proposed TRNG passes the NIST SP800-22 and the NIST SP800–90 B tests with a passing rate greater than 98% and a minimum entropy index per bit greater than 0.99. It also passes the relevant performance tests including autocorrelation test, restart test, deviation test, and temperature and voltage fluctuation test, and it has low hardware overhead.
Numerical realization of automatically reconfigurable optical waveform generator based on photonic reservoir computer

Based integrated schemes, the stable, reconfigurable, high-tolerance optical waveform generators are major challenges for traditional schemes. In this paper, a scheme of automatically on-chip reconfigurable optical waveform generator based on a 4 × 4 photonic reservoir network of waveguides is proposed. To increase fabrication tolerance and target flexibility, this scheme exploits parameter customization of reservoir output layer using particle swarm optimization (PSO) algorithm in optical domain. By automatically controlling the parameters of the reservoir, we can numerically realize an optical waveform generator. We obtain six typical synthetic waveforms and the results match the target waveforms practically. In summary, the scheme realizes on-chip optical waveform generator and has high adaptability for target.
Research on the Evaluation of College English Classroom Teaching Quality Based on Triangular Fuzzy Number

With the advent of globalisation in China, English courses have become a key element of the university teaching system, and in order to change the traditional ‘duck and fill’ approach to teaching, reform of university English teaching has become an important project in the construction of the higher education curriculum in China. The classroom model has changed considerably in the course of deepening teaching reform, such as catechism and flipped classroom, which provide an online learning platform for students to study online, thus effectively improving the quality of university English teaching. Based on this, this paper first examines the impact of catechism on the classroom model in the reform of university English teaching, and then proposes some specific reform strategies in the hope of providing valuable references.
Application of VR Technology in Practical Courses of Preschool Education Specialty

In recent years, with the rapid development of science and technology, VR (virtual reality) technology, as the most potential modern information technology, has been widely used in the fields of architecture, entertainment, manufacturing and education. Taking the practical course of preschool education as the teaching research object, this paper analyzes the teaching status and main problems of the course. Aiming at the problems existing in preschool education, such as general shortage of practical teaching resources, many teaching difficulties and difficult to solve, scattered resources and difficult to meet students’ personalized learning needs, Using VR technology to develop “virtual factory”, integrate relevant VR practical teaching resources, build a practical teaching environment with the combination of virtual and real, innovate the VR technology application mode of “training pre-school vocational skilled talents, combining virtual environment with real environment, and supporting practical teaching, innovating practical teaching and carrying out research”, and realize “teaching + real training” The traditional practical teaching mode changes to the mixed practical teaching mode of “Online Autonomous Learning + virtual practical training + face-to-face teaching + real practical training”, and studies the advantages and problems needing attention of the application of VR technology in the course, in order to provide a help for the application and promotion of VR technology in the course.
Trace Element Composition of Molybdenite: Deposit Type Discrimination and Limitations

Molybdenite is a common sulfide hosting many trace elements. Trace elements in molybdenite from individual deposits have been widely used to constrain the source and conditions of ore-forming fluids. However, the relationship between the trace element composition of molybdenite and deposit types has not been well investigated from a large dataset. Here, simple statistics and partial least squares–discriminant analysis (PLS-DA) were used to determine whether different types of deposits can be distinguished by trace elements in molybdenite and what factors control the variations in trace element composition based on published laser ablation ICP–MS data. Molybdenite from porphyry deposits is separated from that from quartz veins, greisen Sn–W, granite vein Mo, and granodiorite Mo deposits. The former is characterized by relatively high Re, Cu, Ag, Se, Pb, Bi, and Te contents, whereas the latter has higher Ni, Co, Sn, Sb and W contents. Molybdenite from the quartz vein Au ± W deposits (Au-dominated), and porphyry Cu–Au–Mo (moderate Au) are separated from other deposits without gold due to positive correlations with Au, Sb, Te, Pb, and Bi for the former. Assemblages of Au–Sb–Te–Pb–Bi in molybdenite are thus useful to discriminate as to whether deposits contain gold and the degree of gold mineralization. Higher oxygen fugacity is responsible for the relative enrichment of W in molybdenite from greisen Sn–W deposits, whereas lower oxygen fugacity results in the relative enrichment of Re in molybdenite from porphyry Cu ± Mo ± Au and Mo ± Cu ± Au deposits. There are some limitations to using molybdenite as an indicator mineral because of the complex occurrences of elements in molybdenite, large compositional variations within a specific deposit type, and an imbalanced dataset. To develop molybdenite as an indicator mineral tool, further work should be carried out to overcome these limitations. This study provides an attempt to classify deposit types using molybdenite trace elements and has important implications for ore genesis research and mineral exploration.
Implementation of Matrix Drone Show Using Automatic Path Generator with DJI Tello Drones

In this paper described the process of designing and applying 100 DJI Tello EDU drones to display various characters following from A to Z and 0 to 9 as letters and words. The drones are divided into four groups, each of 25 drones. Each drone is connected to a wireless access point where the mission control center operates the command to each drone individually in parallel to display the expected letter or words. Python is the programming language used for commanding and controlling the operation. The communication between drones and the mission control computer is done wirelessly with Wi-FiS02.11N technology running at a 2. 4GHz frequency. Each drone is assigned to a specific IP address as an ID based on the unique Media Access Control (MAC) address. To communicate with the drones, the User Diagram Protocol (UDP) protocol is used for the communication. As a result, the matrix drone can perform different flights showing different characters and words including various custom 3D shapes.
Improved Automated Essay Grading System Via Natural Language Processing and Deep Learning

In order to evaluate students’ total skills, educators repeatedly utilize questions based on free text to evaluate students’ total skills. Yet, when correction is done manually errors occur in addition to long time periods used, hard work, high costs and different opinions as to how to correct papers, a single paper is corrected by multiple people to avoid partiality. Thus, the smart system for automatic grading can solve the problem. Here, we present a very advanced system for grading essays automatically. This is based on Natural Language Processing and Deep Learning technologies. Thus, we need a system to automatically grade essays with low costs, less time and more accurate scores. We need thus an inelegant system for correcting essay questions on an automatic basis. We introduced a method which encodes essays in the form of sequential embeddings. We then use a long Short Term Memory Network (LSTM) working in two directions in order to register semantic information. This method also focuses concentration on each essay in order to be taught how to focus on those materials which are authentic in articles. We can also thus get a good proof of the result of prediction. This BI LSTM may be utilized also to produce neural networks which have the sequence information in the two directions: from the future to the past (backwards) or vice versa, which is called Bidirectional Long Short-Term Memory (BI-LSTM) (past to future). In order to train and test, we utilized the popular set of essays presented in the Automated Student Assessment Prize by Kaggle. The smart system used for automatic grading, in our research, predicts grades in an up-to-date manner. Moreover, the smart system for autograding we have proposed has the ability to highlight important words and sentences, evaluate the logical relationships in meaning in a sentence and gives us in advance grades that can be explained.
Research and Implementation of High Computational Power for Training and Inference of Convolutional Neural Networks

Algorithms and computing power have consistently been the two driving forces behind the development of artificial intelligence. The computational power of a platform has a significant impact on the implementation cost, performance, power consumption, and flexibility of an algorithm. Currently, AI algorithmic models are mainly trained using high-performance GPU platforms, and their inferencing can be implemented using GPU, CPU, and FPGA. On the one hand, due to its high-power consumption and extreme cost, GPU is not suitable for power and cost-sensitive application scenarios. On the other hand, because the training and inference of the neural network use different computing power platforms, the data of the neural network model needs to be transmitted on platforms with varying computing power, which affects the data processing capability of the network and affects the real-time performance and flexibility of the neural network. This paper focuses on the high computing power implementation method of the integration of convolutional neural network (CNN) training and inference in artificial intelligence and proposes to implement the process of CNN training and inference by using high-performance heterogeneous architecture (HA) devices with field programmable gate array (FPGA) as the core. Numerous repeated multiplication and accumulation operations in the process of CNN training and inference have been implemented by programmable logic (PL), which significantly improves the speed of CNN training and inference and reduces the overall power consumption, thus providing a modern implementation method for neural networks in an application field that is sensitive to power, cost, and footprint. First, based on the data stream containing the training and inference process of the CNN, this study investigates methods to merge the training and inference data streams. Secondly, high-level language was used to describe the merged data stream structure, and a high-level description was converted to a hardware register transfer level (RTL) description by the high-level synthesis tool (HLS), and the intellectual property (IP) core was generated. The PS was used for overall control, data preprocessing, and result analysis, and it was then connected to the IP core via an on-chip AXI bus interface in the HA device. Finally, the integrated implementation method was tested and validated with the Xilinx HA device, and the MNIST handwritten digit validation set was used in the tests. According to the test results, compared with using a GPU, the model trained in the HA device PL achieves the same convergence rate with only 78.04 percent training time. With a processing time of only 3.31 ms and 0.65 ms for a single frame image, an average recognition accuracy of 95.697%, and an overall power consumption of only 3.22 W @ 100 MHz, the two convolutional neural networks mentioned in this paper are suitable for deployment in lightweight domains with limited power consumption.
Extracting representative subset from extensive text data for training pre-trained language models

This paper investigates the existence of a representative subset obtained from a large original dataset that can achieve the same performance level obtained using the entire dataset in the context of training neural language models. We employ the likelihood-based scoring method based on two distinct types of pre-trained language models to select a representative subset. We conduct our experiments on widely used 17 natural language processing datasets with 24 evaluation metrics. The experimental results showed that the representative subset obtained using the likelihood difference score can achieve the 90% performance level even when the size of the dataset is reduced to approximately two to three orders of magnitude smaller than the original dataset. We also compare the performance with the models trained with the same amount of subset selected randomly to show the effectiveness of the representative subset.
Estimation of Citarum Watershed Boundary’s Length Based on Fractal’s Power Law by the Modified Box-Counting Dimension Algorithm

This research aimed to estimate the length of the Citarum watershed boundary because the data are still unknown. We used the concept of fractal’s power law and its relation to the length of an object, which is still not described in other research. The method that we used in this research is the Box-Counting dimension. The data were obtained from the geographic information system. We found an equation that described the relationship between the length and fractal dimension of an object by substituting equations. Following that, we modified the algorithm of Box-Counting dimension by consideration of requiring a high-resolution image, using the Canny edge detection so that the edges look sharper and the dimension values are more accurate. A Box-Counting program was created with Python based on the modified algorithm and used to execute the Citarum watershed boundary’s image. The values of ε and N were used to calculate the fractal dimension and the length for each scale by using the value of C=1, assuming the ε as the ratio between the length of box and the length of plane. Finally, we found that the dimension of Citarum watershed boundary is approximately 1.1109 and its length is 770.49 km.
Semi-Supervised Active Learning for Object Detection

Behind the rapid development of deep learning methods, massive data annotations are indispensable yet quite expensive. Many active learning (AL) and semi-supervised learning (SSL) methods have been proposed to address this problem in image classification tasks. However, these methods face a new challenge in object detection tasks, since object detection requires classification as well as localization information in the labeling process. Therefore, in this paper, an object detection framework combining active learning and semi-supervised learning is presented. Tailored for object detection tasks, the uncertainty of an unlabeled image is measured from two perspectives, namely classification stability and localization stability. The unlabeled images with low uncertainty are manually annotated as the AL part, and those with high uncertainty are pseudo-labeled with the detector’s prediction results as the SSL part. Furthermore, to better filter out the noisy pseudo-boxes brought by SSL, a novel pseudo-label mining strategy is proposed that includes a stability aggregation score (SAS) and dynamic adaptive threshold (DAT). The SAS aggregates the classification and localization stability scores to measure the quality of predicted boxes, while the DAT adaptively adjusts the thresholds for each category to alleviate the class imbalance problem. Extensive experimental results demonstrate that our proposed method significantly outperforms state-of-the-art AL and SSL methods.
Symbolic Execution of Hadamard-Toffoli Quantum Circuits

The simulation of quantum programs by classical computers is a critical endeavor for several reasons: it provides proof-of-concept validation of quantum algorithms; it provides opportunities to experiment with new programming abstractions suitable for the quantum domain; and most significantly it is a way to explore the elusive boundary at which a quantum advantage may materialize. Here, we show that traditional techniques of symbolic evaluation and partial evaluation yield surprisingly efficient classical simulations for some instances of textbook quantum algorithms that include the Deutsch, Deutsch-Jozsa, Bernstein-Vazirani, Simon, Grover, and Shor's algorithms. The success of traditional partial evaluation techniques in this domain is due to one simple insight: the quantum bits used in these algorithms can be modeled by a symbolic boolean variable while still keeping track of the correlations due to superposition and entanglement. More precisely, the system of constraints generated over the symbolic variables contains all the necessary quantum correlations and hence the answer to the quantum algorithms. With a few programming tricks explained in the paper, quantum circuits with millions of gates can be symbolically executed in seconds. Paradoxically, other circuits with as few as a dozen gates take exponential time. We reflect on the significance of these results in the conclusion.
On the automorphism groups of Lunardon-Polverino scattered linear sets

Lunardon and Polverino introduced in 2001 a new family of maximum scattered linear sets in PG(1,qn) to construct linear minimal Rédei blocking sets. This family has been extended first by Lavrauw, Marino, Trombetti and Polverino in 2015 and then by Sheekey in 2016 in two different contexts (semifields and rank metric codes). These linear sets are called Lunardon-Polverino linear sets and this paper aims to determine their automorphism groups, to solve the equivalence issue among Lunardon-Polverino linear sets and to establish the number of inequivalent linear sets of this family. We then elaborate on this number, providing explicit bounds and determining its asymptotics.
YOLO Based Thermal Screening Using Artificial Intelligence (Al) for Instinctive Human Facial Detection

Covid19 remains the world's greatest public health emergency. It has become indispensable to measure the temperature of people entering or leaving croweded places to ease the identification of potentially infected and to isolate them from spreading and preventing the spread of the ongoing global pandemic of coronavirus disease. This research work is focusing on thermal screening for an automated scanner using Artificial Intelligence (AI) for instinctive temperature measurement on human faces. The framework used for facial detection is known as YOLOv5 which is a family of compound-scaled object detection models trained on the COCO, a large-scale object detection, segmentation, and captioning dataset. YOLOv5 is able to detect several different objects simultaneously by using its available pre-trained models and robustness of detecting faces even at the vicinity of face masks. The research presents the application, training procedure and capability of the Yolov5. This system is not only used for the human face detection, but also for the detection of some commonly-used objects as an extension to its overall application and performance. Yolov5 is readily available to be implemented in Python, the core programming language working under an Ubuntu-based Operating System providing users the best experience. One of the important outcomes of this research work is the development of a Graphical User Interface (GUI) to work alongside the main programme flow.
Integrated Path Tracking Control for Collision Avoidance of Autonomous Mobile Robot with Unknown Disturbance

Autonomous mobile robot (AMR) has been appealed for intelligent manufacturing and automation applications due to their commendable performance. In the presence of external disturbances and modeling parametric perturbations in industrial applications, it is difficult for the considered AMR to accurately tracking on a desired trajectory while avoiding collisions. In order to solve this challenge, this paper proposes an integrated path tracking control based on an improved adaptive decoupled sliding mode controller (ADSMC). Firstly, the path planning is optimized by using the goal biased rapidly-exploring random tree rajectory planning scheme that considers angle constraints and variable step length. Secondly, an improved decoupled sliding mode controller is designed to follow this path. Adapative gains are scheduled online by exploring barrier functions, which maintains the tracking integrated in a small neighborhood of origin in unknown environments. The convergence and stability of the AMR system are guaranteed theoretically. Finally, through several experiments, the effectiveness of the scheme proposed in this paper is verified.