Reviewerly/exHarmony huggingface dataset:
Exploiting Textual Reviews for Recommendation Systems Improvement

Nowadays, the development of the Web has motivated people to provide their opinions in textual form. Such textual information plays a core role in many methods of Recommendation Systems that can improve the accuracy of recommendations. On successful e-commerce sites, customers naturally tend to evaluate most products positively, which causes unbalanced distributions in the numbers of positive ratings compared to negative ratings. The result here is a decline in the accuracy of recommendation models when the ratios of these distributions converge. In this paper, we developed a recommendation model based on text reviews as additional information. In particular, five essential steps have shaped the proposed model. First, data preprocessing was applied, followed by two parallel steps, namely text classification, and topic modeling. Forth, a combination process was performed between the text polarity and the topic probabilities of the text, followed by the implementation of a process of text similarity. Finally, using the Naïve Bayes model after integrating the inferred adjustment weights in the classification score equation led to a recommendation of items most preferable to the target user. Extensive experiments were managed on three Amazon datasets, which showed that our proposed model exceeded all comparison methods in the Top-N recommendation task. Specifically, the improvement ratio ranged approximately from 10% to 61% on Musical Instruments, 26% on Automotive, and 22% on Amazon Instant Video in terms of F1-measure.
QoE Oriented Adaptive Optimization for 360° Virtual Reality Videos

Recently, the viewpoint-aware adaptive streaming methods have attracted more and more attention in the research of 360° VR video transmission over wireless networks. Therefore, in this paper, we design a reasonable and effective system framework for VR video adaptive transmission over future Mobile Edge Computing (MEC) assisted 5G heterogeneous networks. Then, by introducing the tile priority to weigh the contribution of the tile to the user's QoE, a novel priority-based weighted QoE assessment is presented for VR video, which comprehensively considers the video quality, quality fluctuation as well as the error characteristics of the predicted viewpoint. On this basis, we formulate a novel adaptation optimization problem aiming to maximize the sum of the QoE of all users under the delay constraint. To solve it with low complexity, a heuristic algorithm based on lagrange relaxation is developed. The simulation results show that our algorithm outperforms the others and can effectively improve the overall QoE performance of all users, in terms of the video quality, the quality fluctuation and the interruption time.
Joint Receiver Design with Natural Redundancy

A high-speed information transmission system demands a more powerful communication receiver. In this paper, we integrate deep learning algorithms into constructing waveform level receiver, which is capable of harnessing natural redundancy (NR) in raw message sources. Owing to end-to-end learning paradigm of neural network (NN), the proposed NN receiver can accomplish joint symbol timing synchronization and demodulation based on oversampled waveform sequences directly. The prevalent Transformer structures, suitable for excavating sequential correlation of complex sources and waveform sequences, are considered as the backbone of NN receivers. Under the cases of clean label and noisy label, we conceive two novel training algorithms and interpret their principles in theory. The clean label bits are accessible from original source encoding schemes, while noisy label bits are available from the decision results of canonical receivers with independently noised waveform sequences. When noisy labels possess sufficiently small error rates, optimized NN receiver is competitive with that adopting the accurate labels. As for natural language text sources, simulation results show that the proposed NN receivers' performances are superior to the classical receivers without a priori knowledge, especially when high spectral efficiency modulation schemes are encountered and random noise is significant.
Research on Autoencoders for Higher Order Modulation

Aiming at the problem that previous autoencoders (AEs) were generally unable to handle higher-order modulated signals with a input encoded by one-hot, a strategy of segmented encoding of the input data and a parallel-convolutional neural network (PCNN) model are used to apply the AE to high order modulation. The simulation results show that the proposed scheme can handle 4096QAM signals and has an acceptable classification accuracy which is beyond the traditional algorithm. In addition, the Gate Recurrent Unit (GRU) layers are introduced into autoencoder for channel coding and result a huge gain.
Joint Identification of Modulation and Channel Coding Based on Deep Learning

The tasks of identifying which modulation format and channel coding scheme have been utilized by the wireless signal are crucial to intelligent communications, electronic warfare, and spectrum management. Currently, most research on these tasks digs into either modulation identification or channel coding identification, while the joint identification of modulation and channel coding has not been fully investigated. In this paper, we propose two joint identification algorithms based on deep learning to handle the tasks of modulation identification and channel coding identification simultaneously. The first algorithm adopts a successive architecture, in which two neural networks are used to handle the two identification tasks, respectively. The second algorithm exploits multi-task deep learning, with which two identification tasks can be completed using a single neural network. Experiments results show that, considering the candidate set of three modulation formats and two channel coding schemes, both algorithms achieve satisfactory identification accuracy, and the latter is superior to the former with the accuracy improvement of 10%.
Intelligent Control for Obstacle Avoidance in the Self-Driving Car

The keys to autonomous car accident avoidance in crucial traffic settings are motion planning and tracker control. It necessitates not just system operation but also reliable real-time. We merged an autonomous vehicle's motion planning and tracking control system in this paper to provide path planning and tracking for avoiding obstacles. The motion planning is based on a combination of A* and potential function algorithms. The Tracking Control Module is designed based on model predictive control (MPC) using a vehicle kinematics model and neural network to set the steering angle value. The simulation shows that the scheme can generate smooth paths that can be chosen as references to the console. In addition, it is shown by the results that the car in this method can track the reference path even at sharp angles. The maximum vehicle stopping error at the target is less than 0.1 m when the vehicle's desired speed is 40 km/h, and the maximum vehicle stopping error at the target is about 0.3 m when the desired speed of the vehicle is 120 km/h.
Social Recommendation System: A Literature Review

Recommendation System (RS) has played a significant role recently, especially with the growth of information and the development of the Internet worldwide. Due to issues with the recommendation algorithms, new data sources are required. The Social Network (SN) growth has stimulated its use in several areas, including RSs, leading to the social recommendation systems appearance (RS based on SN). Most studies in this field emphasize recommendation algorithms while ignoring the value of social network data and its impacts on the quality of recommendations. This review paper provides an overview of the techniques used to extract information from the social network for enhancing user representation as well as can be regarded as a reference to social recommendation systems in terms of datasets, algorithms, and evaluation metrics.
A Low Complexity Face Recognition System using Discrete Wavelet Transform and Distance Metrics

Face recognition (FR) is a biometric technology used to identify persons according to their facial features. This study proposes an FR system that meets the proper design requirements, including high accuracy, low complexity, and high processing speed. The multi-level Discrete Wavelet Transform (DWT) is used to extract reduced-size faces' features, whereas the recognition is accomplished using the Euclidean distance. The maximal overlap DWT (MODWT) is used as a feature extractor, which is applied to the obtained features of DWT. The experiments show that the results of using various levels of DWT, without and with the MODWT, are somewhat close to each other using the ORL, Yale, and Face95 databases. In comparison, the proposed work surpasses other related approaches; it highly reduced the utilized data, resulting in a small memory occupation, high processing speed, and a high accuracy rate.
Facial Behavior Analysis-Based Deepfake Video Detection using GAN Discriminator

Deepfake is an artificial intelligence-based method for making fake images of people. It works by putting the existing (source) images or videos on the final (destination) images or videos. But recent improvements in deep learning have made it much easier to make fake videos that look real and are convincing with a relatively small amount of data and computing power. As a result of the development of deep learning techniques such as Generative adversarial networks (GAN), Deepfake has become closer to the truth. Many researchers are based on discovering deep fakes that were created by traditional methods. Traditional methods of detection that look for artifacts and pixels that don't match up can't keep up. This paper can detect deepfakes that are perfectly created. It is detected by modifying the GAN algorithm and inverting its function. The discriminator model of a GAN network is used to analyze behavior, facial gestures, and the appearance of an object. The paper is divided into two stages. The first stage is to use a GAN discriminator that has been modified. It is then trained using a deepfake dataset. The second stage is to test the videos by extracting the faces. Next, run it through the GAN discriminator to see if it's a forgery. In comparison to other networks, the GAN discriminator has demonstrated its ability and accuracy in detecting fake videos. The network's accuracy in detecting and distinguishing between real and fake videos is %94.65.
Research on intelligent search algorithm under computer big data software technology

With the algorithms based on the MapReduce model are constantly updated and optimized, and a number of typical algorithms and models have appeared in the academic world. These algorithms can be divided into Map stage algorithm, Reduce stage algorithm and iterative algorithm according to their own characteristics. After in-depth research, we found that the factors that affect the efficiency of algorithm execution include data scale, data structure and algorithm process. Through these discoveries, these different algorithms have different scopes of application. Various algorithms need to be selected according to the data scale and application situation, and then these various algorithms are combined to select the most appropriate intelligent search research.
Pedestrian Detection Algorithm Based on YOLOv3

The current YOLOv3 algorithm also has shortcomings in dealing with pedestrian detection problems, such as pedestrian missed detection, false detection, and pedestrian multi-pose change problems. Therefore, an improved algorithm based on the YOLOv3 algorithm for accurate pedestrian detection in images was proposed. The algorithm introduces the CSPNet structure and the ECA attention mechanism to the original YOLOv3 algorithm, which greatly reduces the amount of computation compared with the original algorithm, and the performance is significantly improved. The experiment in this paper is based on the INRIA dataset,and the experimental results show that the detection performance of the optimized algorithm is better.Compared with the original YOLOv3 network, the optimized algorithm improves the mAP value by 93.89%.
Intent Classification Using Machine Learning Algorithms and Augmented Data

An essential step in a conversational agent is an intent classification of user-generated text input. The purpose of building the intent classifier for a chatbot is to understand the intention of user queries to respond fast and accurately. Robust chatbots necessitate more utterances for an improved training model. Nevertheless, acquiring and annotating data is time-consuming and expensive. This paper investigates machine learning techniques and data augmentation for addressing intent classification. Experiments were conducted on office product's question answering of Amazon using Random Forest, Multinomial Naïve Bayes, Logistic Regression, and Support Vector Machine (SVM). Contextual word embedding with BERT was used for generating new synonym utterances. The main experiments are the comparison of the performance of these methods after augmenting new data. In general, SVM and random forest yield comparable results. Followed by logistic regression. However, the f1 score of the multinomial naïve base is the lowest. Additionally, we discovered that augmenting new utterances had a simple effect on the performance of models.
Deliberative Robotics Behavior Systems: Asurvey

A system that must perform a variety of tasks autonomously across diverse dynamic settings can be labeled as an autonomous robot. Autonomous robots must be able to make explicit decisions in order to properly execute their given tasks across various open situations in the face of uncontrolled interactions with external factors. The goal of implementing deliberative actions is thus to give robotic systems more reliable, adaptable, and comprehensive functionality. This article aims to develop a broad overview of the current deliberative tasks carried out by robots, including goal-based goal reasoning, planning, acting, monitoring, and observing, with the main focuses of the survey being the planning, acting, and integrating processes involved in these. Thus, the study focuses on robots involved in the process of integrating such deliberative functions into their ongoing actions.
Ontology-Based Network Intent Refinement Framework

Intent-driven network (IDN, also named as intent-based network, IBN) allows users to implement policies in a declarative way, instead of treating complete them in the underlying network. However, how to refine intent in natural language remains to be further researched. Although there exist emerging works on intent refinement, the problem of heterogeneous forms of intents in multiple carrier networks is still unsolved at present. As a formalized term set, ontology makes it possible to unify the various terms at the semantic level. The ontology-based architecture of IDN consists of the application layer, the intent-enabled layer, and the infrastructure layer. Users can input intent in the application layer in various ways, and the input intent can be converted into an intent tuple as <Domain, Attribute, Object, Operation, Result>. The intent-enabled layer constructs an intent ontology, on the basis of intent tuple and network configuration protocol (NETCONF) information from the infrastructure layer. In addition, the refined intent can be verified both in natural language processing (NLP) level and the semantic level. In this paper, we design an intelligent intent refinement system to accomplish intent refinement.
Quad-Tree Image Segmentation Technique with Encryption: A survey

Nowadays, in a wide range of computer technology applications, digital image processing methods are becoming more and more significant. Image segmentation is a critical topic in image processing because it is a requirement for any complex operations to be performed on images, such as data compression, data component labelling, and data encryption. Several algorithms and techniques were developed for image segmentation to ensure the simplification of a given problem by changing a complicated image into a more analytical and easier form. However, it is difficult to obtain a perfect segmentation. In this regard, many methods exist that have been applied. This paper presents one of the foundations of Block-based image segmentation techniques, which is the quad-tree decomposition technique, and the applied image encryption on its main types, algorithms, applications, features, and challenges.
MRI-Brain Image Contrast Enhancement based on Comparative of Multiple Histogram Equalization Techniques

The poor image quality in Magnetic Resonance Imaging (MRI), notably lack of contrast between the tissues, artifacts inherent to these types of images, and interindividual variability, could make the image analysis challenging and compromise the clinical diagnosis accuracy. In order to enhance the important image contents by minimizing the noise and maintaining the actual detailed features, image enhancement methods are required. This research compares the majority of histogram-based methods used to denoise and improve brain MRI images, primarily Contrast Limited Adaptive Histogram Equalization (CLAHE), Histogram Equalization (HE), Bi-histogram Equalization with a Plateau Limit (BHEPL), and Brightness Preserving Histogram Equalization with Maximum Entropy (BPHEME) methods. Based on an analysis of the quality measurement metrics Mean Square Error (MSE), peak signal to noise ratio (PSNR), Entropy, and Root Mean Square Error (RMSE), an experimental investigation with the use of real brain MRI images has been conducted. The effectiveness of utilizing CLAHE to enhance the contrast regarding extremely high-quality MRI medical images has been demonstrated in experimental results based on the quality measurement metrics that the average rate for the CLAHE ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{MSE} =542.15, \text{PSNR}=22.21, \text{RMSE} =21.63, \text{entropy}=5.21$</tex> ).
Path Planning for an Autonomous Mobile Robot Using Red Fox Optimization Algorithm

Path planning is a key challenge in the study of autonomous mobile robots. This paper describes a new bio-inspired optimization method known as the Red Fox Optimization algorithm, which generates a path free of collision for mobile autonomous robots in a known environment with static barriers. The actions of the red fox population in nature served as inspiration for this algorithm. Foxes have created a very successful hunting system that comprises two distinct phases. The first is carrying out a worldwide search, and the second is a local search. Global search behavior was implemented to determine the optimum collision-free route between starting position and the goal position. The algorithm was tested through a simulation in a range of static environments. The suggested algorithm demonstrates that the robot can create an ideal semi-smooth path by reducing the number of nodes and successfully navigating to its objective by avoiding obstacles.
UAV Swarm Type Recognition Algorithm Based on Dual-Domain Fusion Features and Feature Selection

At present, the UAV type recognition algorithms in the literature only realize the identification of a single UAV type through the signal characteristics of the communication domain or radar domain, and there are problems such as low recognition accuracy. Aiming at the above problems, this paper proposes a UAV swarm type recognition algorithm based on the fusion characteristics of communication signals and radar signals. First, the high-order cumulant and instantaneous feature statistics of the swarm communication signal are extracted, and the radar track features are fused to construct the UAV swarm feature matrix; secondly, an improved feature selection algorithm-Secondary Screening of Neighbourhood Components Analysis (SSNCA) is proposed to reduce the dimensionality of the fusion feature matrix; finally, a Sparse Autoencoder Network (SAEN) is used for swarm type identification. The simulation results show that the algorithm significantly reduces the dimension of the swarm feature matrix (only 27% of the original matrix dimension); at the same time, when the Signal-to-Noise Ratio (SNR) is 0 dB, the correct rate of identifying five swarm types can reach 88%.
Waveform Recognition of UAV Swarm Communication in Multipath Channel

The electromagnetic environment of UAV swarm communication is complex, and phenomena such as user interference, multipath fading and frequency shift occur. The performance of the waveform recognition algorithm under the traditional AWGN channel is greatly reduced in this scenario. To solve this problem, this paper proposes a waveform recognition algorithm for UAV swarm communication in multipath channel. Firstly, establish the multipath fading channel model of UAV swarm communication under Alpha pulse interference. Then, aiming at the problem of Alpha pulse interference among swarm users, extract the generalized cyclic mean and generalized cyclic spectrum features of the signal, and establish the waveform feature matrix of UAV swarm communication in multipath environment. Finally, the SAE deep neural network UAV swarm communication waveform recognition model is established. The simulation results show that the algorithm proposed in this paper has strong robustness in the complex environment of Alpha pulse interference, multipath fading and frequency shift, and realizes the recognition of six kinds of UAV swarm communication waveforms. At the same time, the recognition accuracy of more than 80% can be guaranteed when the signal-to-noise ratio is -10 dB.
An Optimization Object Detection Model on Brassica Napus Area Based on Image Compression Framework

Using UAV (Unmanned Aerial Vehicle) equipment, it is often easy to take aerial images of brassica napus in the field, and automatically divide the brassica napus areas in the images by the trained object detection network model, which are used for the subsequent research of brassica napus flowering identification. However, the original aerial images obtained from the UAV equipment have a high resolution of about 5427×3078, and each image also takes up more memory space of about 10 MB. In the limit of hardware resource environment, especially in the case of insufficient GPU video memory, if all the original images are used to train the brassica napus object detection model, it will cost a lot of time, and the training process may also fail. To solve the above problems, a modified image compression framework based on deep learning is proposed to process the original aerial images of brassica napus in this paper and compress the storage capacity of the images on the condition of constant image resolution, so as to speed up the training process of brassica napus object detection model. After experimental analysis, the compression ratio of each original image reaches 6.34, and the training time of the brassica napus object detection model is also reduced to 58.7%, achieving the goal of reducing the training time of the model. Finally, the mAP (mean average precision) of the object detection model reaches 97.13%.