Reviewerly/exHarmony huggingface dataset:
Real-time Track and Anomaly Detection in Complex Railway Environment

Autonomous vehicles, these days have been gaining a lot of interest and it seems to promise a safer, more reliable world. Autonomous cars on one hand have bloomed into commercial production, but on the other hand, autonomous trains in particular have not yet been in the limelight. The paper builds around the premise that in unmanned railway technology, perceiving the driving environment in front of the train and identifying the potential safety threats are critical issues. In response, we propose a way based on computer vision to detect the railway tracks in real-time which can be used for safety and automation purposes. Followed by anomaly detection using deep learning based object detection algorithm. We experimentally show that track extracted has good continuity and low noise, and the probable obstacles also get appropriately detected.
A Lightweight Video Summarization Method Considering the Subjective Transition Degree for Online Educational Screen Content Videos

With the popularization of online education, the number of educational videos is increasing, and some shoddy and dangerous videos also become potential threat to the mental health of students and the public security. Therefore, efficient video summarization technology is critical for the analysis, retrieval, and management of online educational videos. Online education videos usually belong to the screen content videos (SCV), and is typical real-time communication system, which urgently needs lightweight technologies with fast speed and low hardware requirements. Therefore, the traditional video summarization method for nature videos or with high complexity cannot be effectively applied for the online educational videos. SCV generated by screen recording the play of the Power Point (PPT-SCV) has also been widely applied in education field. Therefore, taking the PPT-SCV as an example, this paper proposed a lightweight video summarization method for the educational videos. First, the average standard deviation among frames and the variance of frame difference in a video clip which can effectively reflect the content characteristics of PPT-SCV were used to obtain the category of transitions and the key frames. Then, the subjective transition degree in video is used for marking the knowledge importance of each key frames, and a lightweight video summarization method was finally proposed. Experimental results demonstrated that the proposed method can efficiently locate the key frames which is in line with subjective perception.
On construction of prediction intervals for heteroscedastic regression

In a real-world regression problem, the variance usually varies with the location, which brings difficulties in prediction of the response. Existing heteroscedastic regression methods suffer from inaccurate estimation and/or high computational complexity, and cannot be used to construct satisfactory prediction intervals for small or large data. This article focuses on the problem of constructing prediction intervals of the response under heteroscedastic regression models. We apply the reconstruction approach to parameterize the mean and variance functions as forms of kernel interpolation, and use the maximum likelihood estimation method to estimate the parameters. Our method allows a small number of parameters in the parameterization forms, and only requires low computational cost. Based on these estimators and some Bayesian modifications, we provide three classes of prediction intervals. Numerical experiments for both small and large datasets indicate that they are very competitive compared with existing heteroscedastic regression methods in terms of coverage rate and computational complexity.
RM-Transformer: A Transformer-based Model for Mandarin Speech Recognition

A network called RM-Transformer is proposed in this paper for Mandarin speech recognition. The proposed RMTransformer can make full use of features from different layers in the network instead of features solely from the top layer, which is used in the traditional models. Moreover, the proposed network has excellent capability in addressing the ambiguity problems caused by homophone phenomenon in Mandarin speech recognition task. Empirical evaluations have been conducted in two widely used datasets, which are Aishell-l and Aidatatang-200zh. Experimental results can verify the effectiveness of the proposed scheme.
Manus Tremens

Manus Tremens is a structured improvisation for accelerometer-controlled vibration motors, amplified toy harp, and live sound processing. The performer uses two vibration motors to directly actuate the strings of a toy harp. Accelerometers affixed to the performer's wrists affect the vibration intensity of each motor, which enables nuanced dynamic control of the sounds being produced. The design of this system and performance are inspired by the cimbalom, an Eastern European hammered dulcimer instrument. The Manus Tremens system also represents a collaborative approach to sound-making that combines human movements and mechatronic sound actuation.
Microcalcification detection using k-means based clustering within a possibility theory framework

Breast cancer early diagnosis is a major concern for reducing deadly cases. Automation of microcalcification detection is becoming increasingly important given their tiny scale. At this step, a high false negative rate is observed, leading to high ambiguity. In this context, conventional approaches are unable to handle such ambiguity. Possibility theory offers a powerful paradigm enabling to handle a high uncertainty level. Therefore, in this research,new possibilistic modeling strategy for microcalcification detection is proposed. The developed system is based on k-means clustering followed by a fusion of the corresponding possibility distributions, for decision making. For enhancing classification's accuracy, two aspects may be taken into consideration: scattering within classes and the class discrimination power. A high inter-class variance may be regarded as an evidence of good discrimination. Nevertheless, in case of existing highly scattered classes, miss-classifications of samples at the class margins are still encountered, even at high inter-class variance. Clustering solves this problem by redefining classes in a more compact way. Under this paradigm, the clustering optimization is performed using a criteria based on the Area Under Curve(AUC), where the confidence degree level is based on the consistency principle of Dubois and Prade. The above strategy has been applied for the detection of microcalcifications, based on a set of pyramidal and multi-resolution based features. Validation on the Digital Database for Screening Mammography (DDSM) public dataset has been undertaken. The proposed system, which gives 99.4 % detection accuracy, can be used to assist medical practitioners.
Telemetry Parameter Interpretation in Image Form Through Federated Learning

The automatic interpretation of telemetry parameter during the flight can help monitor the status of the aircraft at any time. However, due to the lack of historical data, effective interpretation is very difficult. In this paper, we propose a method to interpretate telemetry parameter in image form through federated learning (FL). Firstly, to simulate the interpretation of human eye, the telemetry data is converted into an image form for feature extraction. Then, image-related dataset is utilized for model pre-training. Finally, FL is called to integrate data from multiple institutions and train together to obtain a higher-precision model. Experiments show that the method proposed in this paper can effectively improve the accuracy of interpretation and reduce the loss.
A High Performance Blockchain PGPBFT Consensus Algorithm in Integrated Circuit Protection Applications

In the fierce global semiconductor Intellectual Property (IP) competition, there is an increasing risk of IP tampering, which hinders the efficiency of IP reuse. Therefore, the effective protection of IP is of great importance. Circuit IP protection chain, which is anonymous, traceable, and immutability, not only solve the security issues of traditional centralized IP transaction data centers but also improves the regulatory system for data misuse. Although the rapid development of IP protection chains is accompanied by higher design efficiency and shorter design cycles, it also imposes higher concurrency of applications and node load capacity requirements on the blockchain. Since blockchain performance can be limited by the lowest performing node, long waiting time or unresponsiveness will occur under high concurrency with node performance differences. Therefore, this paper proposes an improved practical byzantine fault tolerant consensus algorithm based on node performance grouping (PGPBFT). The algorithm utilizes the grouping strategy of the Mamdani fuzzy model to preferentially select high performance nodes to participate in consensus. The experimental results show that the algorithm has 12.23% higher throughput than the traditional algorithm in scenarios with node performance varies and high concurrency requirements, maximizing the utilization of node performance.
Recognition of Arabic digits using a convolutional neural network

Arabic is the most widely spoken language in the world. However, optical recognition of Arabic handwriting by deep learning networks remains inadequate. Recently, some studies have been based on this field and have produced remarkable results in the recognition of alphabets and Arabic numerals. This article focuses on Arabic numbers recognition problem. We use a convolutional neural network with minimal parameters to overcome the overfitting problem. Preceded by the morphological gradient method to detect images contours. This model applies to the Arabic manuscript numbers database, which consists of 70,000 images available in Kaggle [1]. Our model provides 99.80% classification accuracy with a minimum loss of 0.96%.
Quantum Technology for Comparator Circuit

Quantum computing is the fusion of quantum physics and theoretical computer science that makes it more special and super faster than other traditional silicon-based computing systems. In this work, we have designed quantum 2-bit string comparator using the basic quantum AND, OR, NOT, XOR operations that are based on basic quantum CNOT, V, V+ gate operations. The proposed quantum comparator take the advantages of basic quantum computing to perform parallel processing and super-fast calculations.
Analysis of the Psychological Barriers to Spoken English From Big Data and Cross-Cultural Perspectives

College English teaching aims to cultivate students' comprehensive ability to use English. The study of spoken English barriers is a hot topic in this subject area. Based on a survey of non-English primary college students' spoken language impairments, this paper analyzes the research status of spoken language impairments at home and abroad. It relies upon the theoretical basis of Swain's output and Krashen's input hypotheses. With extensive data mining in colleges and universities as the entry point, this paper's content, object, and method are determined by combining qualitative and quantitative research with empirical research. Through the combination of classroom observations, questionnaires, interviews, and other research forms, this paper concludes that the spoken language barriers of non-English primary college students include language and non-language barriers. The influencing factors of the spoken language output barriers include subjective and objective aspects. The questionnaires are analyzed from the three dimensions of the students, schools, and education departments. This paper proposes some ways to overcome the spoken English barriers of non-English college students. It also suggests that non-English college teachers should pay more attention to the cultural transmission of English-speaking countries in English classes, cultivate students' cross-cultural awareness, and enhance students' enthusiasm in English learning. These actions are more conducive to overcoming the psychological barriers in spoken English output.
Topic modeling and Sentiment Analysis-based Recommender system: A literature review

Recommender Systems (RSs) are widely used to support decision-making process for users by providing recommendations to help them choose the suited products or services based on their history preferences and profiles. The difficulty of obtaining personal user history has brought into attention the need to refer to another alternative when designing a RS and not simply relying on users' ratings. Over the past decade, various websites provided a sharing space for users to freely give reviews and textual recommendations on various commodities. The reviews may contain rich information related to the item or a specific feature that can be extracted using natural language processing techniques. This survey provides an overview of RSs as well as possible techniques that could be deployed on user reviews to enhance the recommendation accuracy. We mainly focus on two techniques: Topic modeling which extracts main features or topics of a given document and Sentiment analysis which studies user polarity through their associated textual input. We show the advantages of associating these approaches with RSs and discuss their main applications, challenges, and open research directions.
Basic analysis for base-band and pass-band transmission system

The main type of communication system can be divided into base-band transmission and pass-band transmission. For baseband transmission, polar NRZ coding is utilized among common PCM coding schemes to achieve the best error performance when the SNR stays the same. Matched filter receiver is adopted to carry on error analysis. For pass-band transmission, signal-space is introduced to simplify the procedure with a few examples. Then a new Euclidean space is mentioned, which is sort of different from the former, to directly map sending bits with their actual position on the space. 2D and 3D space, related to the 4-ary system and 8-ary system respectively, is depicted to illustrate the point. At last, a potential application for pixel maps is considered to qualitatively underscore the error impact on the practical system.
Graph based method for Arabic text summarization

The amount of Arabic textual data is growing tremendously, hence the need to reduce it with the aim to be easier to use while keeping only the necessary from the original text. In this regard, several natural language processing researchers are working on the generation of extractive and abstractive summary tools to achieve this aim. In this work, we explore an extractive approach to realize a generative model of summaries for Arabic single-documents. We focus on the use of graph-based methods to find the most important sentences and then extract them with a variety of text representation methods such as TF-IDF, fastText, and Word2Vec-, similarity measures, and graph ranking methods. To test our system we used the EASC (Essex Arabic Summaries Corpus) and the ROUGE metric to evaluate it. The results obtained show that the TF-IDF representation, the ranking by PageRank, and the use of cosine similarity achieve good performance, which can generate a high-quality summary.
A new decision tree pre-pruning method based on nodes probabilities

One of the most well-known and effective data mining approaches is the decision tree. Many researchers have established and thoroughly investigated this technique. On the other hand, some decision tree algorithms may yield a complex structure that is difficult to comprehend. In addition, data misclassification is common during the learning process. Pruning can be utilized as a fundamental procedure to solve this problem. To improve generalization, it eliminates the use of noisy, contradictory data. In this paper, we propose a new pre-pruning method that prunes weak nodes with a high probability. The experimental results are verified using 24 benchmark datasets from the UCI machine learning repository. The results indicate that our new tree pruning method is a feasible way of pruning decision trees.
Secure sharing method of network data transmission based on multi-layer encryption algorithm

In network data transmission, the data to be encrypted is not always at the same level. The access structure is embedded in the ciphertext, resulting in excessive communication and computing overhead. Therefore, a secure sharing method of network data transmission is proposed based on multi-layer encryption algorithm. According to the relationship between users, type servers and service providers, the logical structure of data transmission is established, and the data sharing contract is called on the basis of secure communication. By designing the key synchronization mechanism, both sides of the transmission can use the same key for encryption and decryption to ensure the forward security of the data. The key distribution scheme is established based on multi-layer encryption algorithm. The ciphertext information of the next layer is directly embedded in the ciphertext of the previous layer, which realizes the jump transmission of ciphertext and ensures the security of data privacy. In the final stage, both the master node and the slave node will receive the confirmation information, and then check the signature of the confirmation information to complete the secure sharing of data transmission. The test results show that this method reduces the communication overhead and computing overhead, and improves the efficiency of encryption and decryption.
Image Retrieval-Based Localization Under Seasonal Changes

In this paper, we achieve the image retrieval-based localization under seasonal changes by learning appearance invariant representation of location with deep neural network. Specifically, we construct the Siamese network architecture to perform the similarity learning between different seasonal images. For each branch of the network, there are three main components. First, a full convolutional network is utilized as local feature extractor to generate rich local features. Then, the NetVLAD layer is utilized to aggregate local features into a global feature. Finally, an fully connected layer is utilized to reduce the feature dimensionality. During the training phase, the weighted soft margin ranking loss is introduced mainly for the convergence acceleration. To show the performance of our method, we have done comparative experiments on the NordLand dataset. The quantitative and qualitative results show that our learning-based method obviously outperforms the traditional hand-crafted methods under seasonal changes.
Research on video summarization method based on convolutional neural network

Short videos on the Internet are growing exponentially, and the number of videos uploaded every day is huge; people also involve a lot of video data in real life. People can retrieve and view all kinds of videos, but it also brings a lot of problems. On the one hand, the accumulation of a large number of videos makes people unable to find the videos they want quickly, and the repeated scenes in the videos will also waste people's time and energy; on the other hand, a large amount of video data also brings enormous pressure to storage. Aiming at the problems of inaccurate selection of key frames and how to select video frame features in existing video summarization models, this paper proposes a multi-feature-based video summarization generation model (DME-VSNet), which extracts multiple features of video frames. Including importance score, image memory strength and image entropy. Aiming at the problem of inaccurate video shot segmentation, this model proposes a video shot segmentation algorithm based on TransNet network, which divides the original video into several short shots through shot boundaries; the model inputs the above three features into the proposed The video frame score is obtained in the MLP architecture, and the key frame is selected by the score to generate a video summary. The effectiveness of the video shot segmentation method based on TransNet network and the overall model based on convolutional neural network is verified by comparative experiments. The experimental results show that the evaluation results of the video summaries generated by the three features are better.
Application of deep learning in the detection and practical evaluation of ski resort teaching area

Summary The teaching land of ski resorts provides learning sites for beginners of skiing. The teaching sites of different ski resorts are different. When selecting teaching scenes, we need to focus on the safety of the site. The focus of our research is to detect more suitable teaching land in the whole ski resort and evaluate its practical indicators. In order to accurately detect the teaching area in the snow field, we propose a method based on deep learning. The algorithm innovatively optimizes the convolution network. The experiment combines convolutional neural network with recurrent neural network for training, and uses the output of convolutional neural network to train recurrent neural network, so as to continuously predict the next optimal teaching area in the sequence. The purpose of training is to optimize the detection result area. The experimental results show that the model can better detect the sites suitable for teaching in the snow field.
LCNet: Location Combination for Object Detection

Object detection is a widely studied task in the computer vision field. In recent years, some milestone approaches and solid benchmarks have been proposed, which significantly boosts the development of related researches. The previous object detection methods follow a paradigm: the classification head and the regression head share the same feature extracted by the backbone network. In this paper, we revisit this paradigm for two-stage detectors and prove that the regression head can achieve better results by using the local features. In our proposed Location Combination Networks (LCNet), we extract the effective region of the feature in a Laplace way, and we introduce auxiliary confidence gain loss, Intersection over Union (IoU) gain loss, and distribution loss to guide its convergence. In the classification head, we combine these local features into the global feature for better classification. In the regression head, by ranking these effective regions in the spatial dimension, we can select the local features closest to each foreground boundary and use the selected features to predict the offset of each foreground boundary. Finally, we combine the locations of the four boundaries to obtain the final bounding box prediction. Extensive experimental results on the MS COCO benchmark validate the effectiveness of our proposed method.
A social recommendation based on GCN improved by social sampling

To avoid data overload, recommendation systems have been created. Due to the difficulty of data collection, the recommendation system faces a cold start and needs to introduce auxiliary information. In this paper, we use social recommendation to solve the cold start, and we adopt a graph convolutional neural network to aggregate high-order neighbors and sample the neighbors for the auxiliary recommendation system. Ultimately our model achieves impressive results on classical datasets. Compared to the baselines, we achieved a higher accuracy rate.