Reviewerly/exHarmony huggingface dataset:
A Self-Attentive Interest Retrieval Recommender

Thanks to the attention mechanism, self-attention networks (SANs) have been widely used in sequential recommendation. However, most existing SANs approaches still follow an old fashion generating one single embedding as final representation, which constraints model’s capacity. To enrich this kind of representation, sequential recommender uses metadata such as item category to capture user’s multi-interests. But this method will not reach its expectation due to item’s long-tail property. This property will result a large constant of category cannot be effectively activated by the lack of interaction records. Another drawback is that may also lead to over-parameterization caused by the massive categories. Particularly, we propose a Self-Attentive Interest Retrieval network (SAIR) to explore a context-aware representation from user’s behaviors while not fall into over-parameterization. SAIR works in a typical SANs manner, encode the behavior sequence using self-attention, and we propose an interest retrieval module to project the sequences to an interest relevance distribution adaptively. And we leverage an interest-to-interest interaction to generate several context-aware interests embeddings. Then we fuse multi-interest embeddings as final output. Extensive experiments are carried out on three real-world datasets, the results demonstrate that SAIR outperforms other SANs methods and other state-of-the-art algorithms in multiple evaluation metrics.
A Framework for Cybercrime Prediction on Twitter Tweets Using Text-Based Machine Learning Algorithm

With the rise in popularity, social media platforms such as Twitter are more recognized for calling tweets to construct user networks that can communicate. Because of the increasing number of Twitter users and the expansion in cybercrime rates. Cybercrime is a crime perpetrated using technology. It is feasible that the social networking platform might be utilized to commit a crime. The researchers focused on cyberbullying and cyberthreat, given the severity of cybercrime's effects on victims and given the devastating effects of cybercrime on victims, it is critical to devise effective methods for predicting and preventing it. Made advantage of the Twitter API and the tweepy python module to extract a real-time tweet from Twitter. In order to improve their ability to detect cybercrimes and sentiments, through employed an online source dataset to build a model. The goal of this study is to create a machine learning strategy for anticipating cybercrimes. Find the approach that best fits the data by comparing it to other methods. To evaluate the accuracy and dependability of the trained model, through gathered data and information from dependable sources. The significance of the research is shown by the analysis of the data.
A Method of Depth Prediction Based on PDB-ConvLSTM

In order to solve the problems that motion features are ignored in the current deep learning methods for depth prediction and the coding module based on maximum pooling may lose details, a depth prediction method based on Pyramid Dilated Deeper ConvLSTM (PDB-ConvLSTM) is proposed in this paper. The method combines delated convolution with Convolutional LSTM(ConvLSTM) to learn the time information of image sequences and retains the details of image to a greater extent. Experiments on the KITTI standard dataset show that the proposed method improves the accuracy of percentage of right pixels of depth prediction by 5% on average compared with other existing depth prediction methods.
CAGSF: Optimization Algorithm for Network Representation Learning Based on the Community Structure

Network representation learning is one of the research hotspots of complex network theory, which mainly solves the coupling problem of traditional complex network representations. Many classical network shallow representation learning algorithms have achieved good results in some downstream tasks. However, the node representation learned by these algorithms generally has the problem of noise because the structural information of the community is not considered. Therefore, this paper proposes a network representation learning optimization algorithm based on the structure of the community: CAGSF. Firstly, the classical community partition algorithm is used to obtain the community structure information of the network. And then we use this information to smooth the noise generated in the node representation process, so that the nodes in the same community can be represented as much as possible. Experimental results show that the algorithm improves the performance of the four classical network representation learning algorithms by up to 5% and 4.73% in the tasks of node classification and link prediction.
T2V-TransD: A Method of Temporal Knowledge Representation Learning Based on Mapping Matrix

The purpose of knowledge representation learning(KRL) is to represent the symbolic knowledge graph with a numerical vector. The traditional knowledge representation learning method learn the representation of triples without temporal information. While on temporal knowledge graphs with temporal information, traditional methods cannot effectively utilize temporal information. To address this problem, researchers have proposed temporal knowledge representation learning(TKRL) methods, but most of the existing methods represent entities and relations in the same vector space, which limits the effectiveness of the models. In addition, some methods model for point time information, which cannot effectively handle temporal information of time intervals. Therefore, this paper proposes a mapping matrix based learning method for temporal knowledge graph representation. In this method, temporal information is represented by vector, which is used to construct mapping matrix with relation vector, and entities are mapped into the relation space through the mapping matrices. The experimental results show that on the link prediction task of the temporal knowledge graph, the method in this paper can effectively utilize temporal information. For example, on the dataset Wikidata12k with time interval information, compared with the model TransE, Hit@10 increased by 10.8 percentage points, Hit@1 increased by 4.4 percentage points.
The Construction of Information Technology in Universities under the New Situation

From the beginning of information technology entering the campus, the application of information technology has injected new vigor into the study, life and scientific research of university teachers and students, realized the digitized transformation of teaching, improved the ability of university administration, and promoted the overall development of the university. With the rapid development of information technology, information technology in universities has also ushered in a period of change. The original information technology in universities can no longer meet the soaring information needs of teachers and students. In addition, the in-depth mining of “data value” and the wide application of “big data” expose many drawbacks of university information technology in data application. The transformation of university information technology is imminent.
Purification and Multi Temporal Semantic Network for Continuous Sign Language Recognition

Continuous Sign Language Recognition (CSLR) is a typical weakly supervised task, which aims to convert a sign video into a gloss sequence. However, there is lack of clear segmentation points between gestures in sign videos, so it is not easy to obtain the time information of gloss. Existing CSLR models usually extract gesture-wise features with a receptive field of single time granularity, which causes inconsistent segmentation and local ambiguity issues and becomes a bottleneck in the entire model. This paper proposes a Purification and Multi Temporal Semantic Network (PMTSNet) to handle the local consistency and context dependency problems. Specifically, the proposed model first extracts frame-wise features of sign language videos using 2D convolutions and then captures gesture-wise features from sign video segments of different temporal granularities. The obtained gesture-wise features are then fed into BiLSTM to get gloss-wise features by modeling the context dependencies. Then, an attention based purification module selectively combines fine-grained gesture and coarse-grained gloss information to obtain features with richer semantics. Finally, the model is trained using multi knowledge distillation connectionist temporal classification loss, which further enhances the performance. The experimental results on the RWTH-PHOENIX-Weather-2014 dataset show that the proposed model outperforms the state-of-the-arts.
REIPIC: New Method for Reshaping the Entropy and the Frequencies Pattern of a Random Variable and Its Performance Evaluation Using SVM

Some applications require adapting the randomization of a dataset to resemble that of a reference one, which its generation equation could be unknown. REIPIC is a new method that solves this issue by iteratively treating the frequencies pattern of the values of the random dataset within a sliding window of a specific size, to resemble their counterparts in the reference dataset. An entropy-based SVM is used to evaluate the REIPIC adaptation performance at each iteration, and it decides the termination of the REIPIC procedure when reaching a specified distinguishability threshold. REIPIC first declaration is to investigate how the detection is affected by reshaping the entropy of the covert channel which exploits the randomization of the IP Identification header field, to update the intrusion detectors awareness. REIPIC is the first presenter for the frequencies pattern of a dataset in a matrix form, and it provides a successful conceptual framework that could be exploited in various random pattern adaption applications.
Joint Contextual Transformer and Multi-scale Information Shared Network for Crowd Counting

Crowd counting is a fundamental task in computer vision with applications for video surveillance and public safety management. However, scale variation remains a challenge. To overcome this weakness, we propose a contextual transformer and multi-scale information shared network (CTrans-MISN) to improve the performance of the counting. It consists of (i) a contextual transformer, capturing the contextual information of crowds, that makes the network more powerful to face the scale variation. And (ii) with the help of multi-scale information shared module, the model can learn more conducive information to identify congested crowd scenes accurately. We finally assess the proposed method on four public counting datasets. The extensive experimental results demonstrate the superiority of our approach over other compared baselines.
Grasshopper Optimization Algorithm Based on Adaptive Curve and Reverse Learning

The disadvantage of the grasshopper optimization algorithm (GOA) is its insufficient ability in global exploration, relatively slow convergence speed, and easy to obtain the local optimal solution. Aiming at the poor convergence accuracy of GOA algorithm, a new grasshopper optimization algorithm(OLCZGOA) based on adaptive fusion curve and reverse learning was proposed. Firstly, an improved curve adaptive formula is introduced to replace the linear adaptive formula of parameter C in the grasshopper optimization algorithm to improve the convergence speed of the algorithm. Secondly, considering that grasshopper optimization algorithm is easy to obtain local optimal solutions, three selection strategies are introduced to reverse learning, which makes grasshopper optimization algorithm have stronger global optimization ability. In this paper, nine test functions are selected to test the proposed improved algorithm. The results show the effectiveness of the proposed improved strategy, and the OLCZGOA algorithm has better solution accuracy compared with other comparison algorithms.
A Fiber Design Automation Platform Based on Deep Learning

An optical fiber design automation platform based on deep learning is built. It supports the fast, accurate and flexible design of weak-coupling few mode fiber, with arbitrary dispersion single mode fiber and so on.
Global Feature-Guided Real-Time Semantic Segmentation Algorithm

With the development of convolutional neural networks, a large number of high-precision semantic segmentation algorithms have been proposed. However, these algorithms are too complex to infer images in time, and difficult to apply in industrial scenarios. To address this problem, in this paper we therefore propose a lightweight real-time semantic segmentation network which is guided by global features. We first design an inverted residual module to extract the features of the image. Then, the phased feature aggregation module based on the global pooling layer is employed to fuse the features to transmit information from the low level to the high level. Finally, in the feature recovery stage, the image boundary extracted by the Laplacian of Gaussian operator provides boundary information for the deep features to solve the problem of the loss of detailed information, such as boundaries in the continuous down-sampling process of image features. The Experimental results on the CamVid dataset show that the proposed method has a good performance in the inference speed and segmentation accuracy. The inference speed of processing 960×720 images can reach 210FPS, and the segmentation accuracy can reach a mIoU of 68.6%.
HDC-Based Convolutional Neural Network Image Inpainting

Recently deep learning-based methods have been widely used in various aspects of image processing, with remarkable results for the challenging task of filling large areas of missing images. These methods can generate visually plausible structures and textures, but fuzzy filling can also occur when irregular voids are encountered. This is mainly due to the grid effect caused by unreasonable expansion factor setting in the use of neural network and continuous expansion convolution. As a result, the deep convolution operation cannot effectively obtain the details of the initial feature map, resulting in unreasonable repairing effect. Based on this phenomenon, we propose a convolutional neural network structure based on HDC (hybrid dilated convolution). The first stage consists of ordinary convolution and continuous dilated convolution, and the design of the dilated factor in the continuous dilated convolution is following the HDC method, which makes full use of the underlying feature map information while increasing the perceptual field. The second stage of the network consists of a convolutional branch and a perceptual branch, which are in parallel structure. The perceptual branch generates the filled region by extracting the feature of interest from the known region and generating the filled region based on the feature, after which it is aggregated with the feature map generated by the convolutional branch and fed into the decoder and deconvoluted to produce the restored image. Experimental results show that our method produces higher-quality inpainting results in terms of generating coherence, clarity, and visual confidence.
Dynamic Gesture Recognition Method Based on Millimeter-Wave Radar

The millimeter wave radar-based dynamic gesture recognition is the use of electromagnetic waves to obtain the motion characteristics of human dynamic gestures, to achieve automatic recognition of gestures. This paper proposes an frequency modulated continuous wave (FMCW) millimeter wave radar gesture recognition technology, the analysis and processing of radar echo, after the radar signal detection, in a complex environment to extract the gesture and radar distance, speed and other features, to get the radar and target azimuth angle, elevation angle, distance-weighted doppler, azimuth elevation angle covariance, azimuth angle-weighted doppler correlation, elevation angle-weighted doppler correlation. The six features are used to classify the six gestures based on the features using a shallow artificial neural network. It is demonstrated that this method can quickly and effectively identify different categories of gestures and has good generalisation to untrained test sets.
MOD-NAS: Migration-Based Object Detection Neural Architecture Search

Object detection is one of the most concerned issues in computer vision, especially in the real scene, its accuracy, model size, inference speed, IoU and other indicators have strict requirements, so the manual construction of neural network model often can not get good results. This work uses neural architecture search (NAS) as a tool to search the lightweight object detection network that meets the requirements of real scenes by combining manual and automatic methods. In practice, we take the intelligent driving as the background, using classification data sets with proxy, shared weight and migration-based algorithm to search object detection network. The first step to search out the sensitive to intelligent driving of backbone, the second step migrates the backbone to the object detection network, then using the actual data collected by the network further fine-tuning. Finally, a neural network model satisfying the requirements is obtained According to the experimental results, we can see that our work in the model size, precision, inference speed and so on comprehensive index made a balanced and excellent results, proves the effectiveness of this method, also represents this work for the practical application of neural network architecture search provides effective solution.
A Lightweight Object Detection Method for Bank Operation and Maintenance Scenarios

Due to the complex background of bank operation and maintenance scenarios, it is extremely difficult to detect and analyze the object of operation and maintenance scenarios. The traditional target detection algorithms, such as F-RCNN, are not suitable for object detection. We propose a lightweight object detection method for bank operation and maintenance scenarios based on deep learning. The proposed method highlights two improvements based on Yolov5. The first adjustment of it is this method constructs a new convolution method and a new activation function. Also, we use the multi-scale output strategy of Feature Pyramid Networks (FPN) to develop a new lightweight feature extraction algorithm. These changes we made are based on the real-life situation, such as bank operation and maintenance scenarios. Experiments have been carried out on the same data set with multiple object detection algorithms, and the experimental results show that the proposed method reduces the complexity of the activation function. Meanwhile, the module is flexible, and can be easily applied to other similar business scenario detection models.
CapGAN: Medical Image Captioning with Clinical Style and Pathology Preservation Using Conditional Generative Adversarial Nets

In the process of clinical diagnosis, radiologists are mainly to analyze the obtained medical images and yield corresponding disease diagnosis reports. In order to reduce workload and improve efficiency while generating high-quality diagnostic reports, we proposed a novel method to achieve medical image captioning based on conditional generative adversarial nets, in which language evaluator is introduced to align language styles. The proposed method consists of three modules, i.e., diagnostic report generator, discriminator and language style evaluator. Specially, diagnostic report generator which contains feature extraction, attention mechanism and report output modules is used to generate diagnostic reports which is desired to appropriate clinical reports. Then, RNN-based discriminator is used to determine the authenticity between the generated and true diagnostic report. Besides, language style evaluator is adopted to keep the style consistence between the generated and real diagnostic report. Additionally, we use reinforcement learning mechanism to yield high-quality diagnosis report and overcome discretization problem in adversarial training. Extensive experiments show that our proposed method gains significant improvement over other methods in medical image caption. Objectively, ours achieves average increments of 10.15% BLEU-1 and 13.06% BLEU-2 on Open-i. A similar trend can be found on LGK. Additionally, ours outperforms than other comparisons language style evaluation by clinical radiologists subjectively.
SGDANet: Gland Instance Segmentation Based on Spatial and Geometric Dual-Path Attention Modules

Gland instance segmentation is an essential step in quantitatively analyzing the malignancy degree of adenocarcinomas [1] by pathologists. Automated gland instance segmentation is in great demand. Despite the great progress of CNN-based methods, they are still prone to inaccurate segmentation when dealing with adjacent glandular cells with large morphological differences. To address this issue, this paper extends Mask R-CNN and proposes SGDANet. It proposes a novel and effective geometric module for better capturing geometric features of the cell membrane, cytoplasm and nucleus. It also designs a parallel spatial-geometric dual-path attention mechanism for better feature extraction. To make the dual-path attention work, SGDANet integrates the fine features with coarse features through a new fusion module to better preserve details. We conducted experiments on Glas and CRAG public datasets. SGDANet not only achieves higher F1 values than the state-ofthe-art gland segmentation methods but also outperforms those applied latest instance segmentation networks in the mAP metric.
A Giant Panda Face Alignment Method Based on an Improved FAN Network

Alignment of giant panda face is a key step in individual identification of giant panda faces. Effectively aligning key points on the giant panda face can help accurately capture information across the entire giant panda facial region, which is conducive to better individual recognition of giant panda faces. Aiming at the problem of lack of effective panda face alignment methods, this paper proposes a panda face alignment method. The alignment method is based on an improved FAN face alignment network model, with the convolutional layer structure of the residual module modified and combined with the channel attention module to make it more suitable for the Panda face alignment task. The experimental results show that the improved alignment network model achieves a Mean Normalized Error (MNE) loss of 4.69%, a failure rate of 27.7% at a threshold of 5%, and a failure rate of 1.80% at a threshold of 8%, which is better than the original FAN network. At the same time, it can align not only relatively straight images of giant pandas, but also images with large pose changes or occlusions, and it has good robustness.