Reviewerly/exHarmony huggingface dataset:
Intelligent English Course Evaluation System Based on BP Neural Algorithm

Course evaluation is a relatively complex, fuzzy and non-linear process involving a large number of influencing factors. Introducing appropriate intelligent optimization methods in the course evaluation system can make the evaluation results more scientific and credible. The aim of this paper is to study the evaluation system of online business English audio-visual courses based on BP neural algorithm. Based on the basic principles of BP neural networks, the evaluation indexes and models of online business English audio-visual courses based on BP neural networks are established, and case studies are carried out to provide references for improving teachers' teaching level, optimising teaching platforms and improving teaching quality. On the whole, the model proposed in this paper is feasible and can provide a reference for the development of business English online education.
Full-scale feature fusion dark-light image enhancement network

Aiming at the problems of narrow dynamic range, serious noise and color deviation in dark light images, a dark light image enhancement network based on supervised learning is proposed. The enhancement process is divided into three steps: layer decomposition, reflection recovery, and illumination adjustment. Combined with Retinex theory, three-layer Unet++ is designed to decompose the image into reflection component and illumination component. The reflection recovery firstly combines the light-guided attention mask to assign new weights to the reflection components, and then sends them to the full-scale U-shaped network for image denoising and detail restoration. Illumination adjustment adjusts the illumination adaptively. This paper is validated on several datasets with improved subjective and objective evaluation results, which can effectively suppress noise and distortion problems, and significantly improve image brightness and quality.
Equipment anomaly detection method under cloud-edge collaboration model based bi-directional long short-term memory and variational autoencoder

Aiming at the problems of large amount of heterogeneous Industrial data, high fault concealment, complex feature engineering of traditional methods, an anomaly detection method combined with Bi-directional long-short term memory, variational autoencoder and whale optimization algorithm based on cloud-edge collaboration. By integrating the output of each detection models with different dimensions through the residual weight matrix, to obtain the comprehensive residual value, and compare with the residual threshold for anomaly detection. Through the experiments on SKAB and TEP datasets, the results verify the effectiveness and general adaptability of the proposed method, and the anomaly detection accuracy is higher than a single detection model and existing anomaly algorithms such as CNN and LSTM-AE.
Light field saliency object detection based on self-selected multimodal fusion

Saliency detection of light field images is a key technology in applications such as visual tracking, target detection and image compression. Existing light field saliency target detection tends to ignore the complementarity of cross-mode light field data, inevitably introducing redundant information and leading to blurred salient images. Even in similar or confusing scenes, there are problems such as incomplete detection objects and difficult background suppression. To this end, this paper proposes an image saliency detection network based on self-selective cross-modal feature fusion. Firstly, hierarchical features are extracted from the backbone network, and each modal feature is optimized based on the attention mechanism using the spatial alignment and channel rescaling modules, and then the two modal features are fused to obtain a more accurate saliency map guided by edge information. Experimental results on the latest light field dataset show that this method outperforms the comparison method both quantitatively and qualitatively.
Sentimental Analysis of Movie Review Based on Naive Bayes and Random Forest Technique

Rapid advancements in text classification algorithms have kept pace with the exponential rise of digital materials in recent years. In order to automatically extract expressive features, new machine learning algorithms have been suggested that take advantage of recent developments in deep learning techniques. Rapid progress in this area has spawned a multitude of ways for translating human speech into machine-readable data. Ad hoc pre-processing processes are utilized in conjunction with state-of-the-art language modelling algorithms; nevertheless, their presentation is generally glossed over in favor of a more thorough explanation of the classification stage. This work aims at building a model that is used to analyze sentiments, where accuracy is tested by taking data sets of positive and negative movie reviews. Proposed work is divided into 3 tasks: Data Extraction, Preprocessing and Modelling. We have used text classification techniques of Machine Learning by making changes in text vectorization using BOW (Bag Of Words), N-grams and TFIDF and modelling is built using Naïve Bayes and Random Forest algorithms. Our findings demonstrate that the suggested model provides superior performance in terms of accuracy, precision, recall, and f-measure. Using cutting-edge methods, proposed model achieved competitive results on the IMDB movie review dataset.
Security Analysis of Conventional Attack by Suitable RFID Based Deep Learning Method in Industrial IoT

Radio Frequency Identification (RFID) is a technology for tracking and identifying objects, creatures or people even in a harsh environment. This technology makes use of a electromagnetic field generated by an RFID reader to identify and locate tags attached to objects. With its advantages such as long-range sensing, high mobility, and overall cost effectiveness, RFID has been widely adopted by the industrial Internet of Things (IoT) industry. Recently, RFID based deep learning methods are emerging as promising solutions for industrial IoT applications. Deep learning, a type of artificial intelligence, uses large datasets to accurately and quickly identify objects and patterns. By combining this technology with RFID, industrial IoT applications can address challenges such as efficient resource management, production optimization, quality assurance and maintenance. RFID based deep learning methods have already been implemented in various industries such as manufacturing and logistics, wherein it efficiently tracks and monitors resources, processes, and personnel. With the advancement of this technology, future industrial IoT applications can offer greater efficiency, scalability, and reliability.
A Novel QoE Indicator for Mobile Networks based on Twitter Opinion Ranking

Mobile networks have provided several services for users, such as, video and voice calls, communications by social networks, among others. Thus, there is a need to understand the Quality of Experience (QoE) of the mobile users customers. In this context, studies try to measure the users QoE about mobile services. This research aims to search for new QoE indicators for the main mobile network operators in Brazil based on users opinion in Social Networks. Machine learning algorithms are used in this work to process the users opinions, using Natural Language Processing (NLP) with Bidirectional Encoder Representations from Transformers (BERT) in the Brazilian language, the BERTimbau, to classify the opinions according to the MOS score, and therefore to arrive at new QoE indicators. The experimental results, executed in the predictive model with an accuracy of 0.80, point to a parity in the QoE indicators by operator.
Same-radical Character Association Model in Text Classification

Research has verified that associative learning will improve learning efficiency during the process of recognizing Chinese characters. Therefore, some scholars believe that the associative information guided by radical molecules is the key to ensuring semantic understanding robustness and have incorporated it into their models. However, characters with the same radical are also often associated and widely used as contextual cues to enhance semantic understanding. This indicates that the information of the same radical has a positive effect in learning text. From the perspective of semantic complementarity between characters with the same radical, we drew inspiration and proposed a novel Same-radical Character Association Model (SCAM) for text classification in Chinese. SCAM aims to enhance model learning and training of text classification tasks by integrating same-radical characters. The model can be segmented into three processors, same radical extension processor, critical feature selection processor and cognitive and prediction processor. Experiment results shows the proposed model excels existing system in CNT, TNT and THUC databases, which indicates that integrating same-radical information will improve the Chinese text classification tasks.
A Novel Wireless Vehicle Velocity Measurement Method Based On Machine Learning

High-precision vehicle velocity measurement methods typically require additional context, such as the rough location of the target vehicle. In the Internet of Vehicles domain, it is possible to achieve real-time vehicle velocity measurement based on the characteristics of wireless communication signals without any extra context. In this paper, we first propose a velocity measurement algorithm utilizing a convolutional neural network (CNN) with the differential constellation diagram of the wireless communication signal as input. We also develop an acceleration algorithm based on gated recurrent units (GRUs) that achieves higher accuracy than the CNN-based algorithm under high signal-to-noise ratio conditions. Subsequently, we propose a hierarchical model that can attain the highest accuracy, albeit with increased complexity. Finally, we analyze the theoretical upper bounds of accuracy for both the CNN-based and GRU-based algorithms. The performance of proposed algorithms is validated through simulations. Compared to signal based measurement methods, the proposed method has higher accuracy and has no requirements on synchronization. Moreover, the complexity of the GRU-based algorithm is lower.
Fringe Estimation in Distributed Scatterer Interferometry

In distributed scatterer (DS) interferometry, fringes within a multi-looking window can cause bias on coherence estimation and degrade phase-linking precision. Furthermore, fringes might also bias the estimated consistent phases. The problem can be solved by estimating and removing these fringes, i.e., defringing; however, imprecise estimation can significantly affect the results. Thus, a new defringing method for DS time series analysis is proposed. Different from the previous defringing methods, the new method makes use of the information from redundant interferometric combinations under the assumption of Gaussian speckle, and a series of consistent fringes with single reference can be obtained. The basic idea is to establish a framework of weighted least square adjustments with gross error elimination, exploiting the triangular consistency of fringe frequencies as a constraint to estimate fringes. The statistical properties of fringe frequency estimation were exploited to determine the optimum choice for the weight matrix. The quality of the estimated fringes can be improved significantly using the proposed method and therefore the quality of consistent phases in non-stationary areas can be increased. Simulated and real data experiments demonstrate the effectiveness of this new defringing method.
Privacy Preserved and Decentralized Smartphone Recommendation System

The increasing popularity of mobile phones has led to an abundance of online reviews, making it challenging for consumers to make well-informed purchasing decisions. This study proposes a novel recommendation system-based mobile phone rating classification approach using federated learning and Term Frequency-Inverse Document Frequency (TF-IDF) features. We created a novel dataset by scraping over 13,000 mobile phone reviews from Flipkart’s website. The proposed approach involves the development of a federated deep neural network (FDNN) to classify the newly created Flipkart dataset. This approach includes data cleaning, balancing, TF-IDF feature extraction, and prediction using federated learning. We employ two clients and one server and conduct three rounds of experiments. The experimental results demonstrate that the proposed approach achieved an accuracy of 96.68% on the aggregated server side while maintaining the security of customer data on their local devices. The proposed approach has the potential to assist consumers in making well-informed purchasing decisions and can be extended to other e-commerce platforms with large datasets of online reviews.
10.4 Muslim Fathers Speak to Their Infants: Newborn Behavioral Observation

The Muslim father is a provider. He also has the responsibility for guiding his children to live an ethical, moral, and spiritual life. In low-resource communities, the burdens of poverty, prejudice, gender bias, and other risk factors can add weight to these fathers’ sense of responsibility. Such circumstances can interfere with the critical bonding experiences of the father with his newborn and change a joyful time in family life to a period of stress and fear for the future. Interactive infant-focused, collaborative parenting tools such as the Newborn Behavioral Observations (NBO), have the potential to foster positive parent-infant interaction, thereby introducing a note of tenderness and hope into the newborn period. Because the quality of early experiences shape brain development, well-being, and resilience, we explore practical strategies for Muslim fathers to connect with their infants. Two brief videos will highlight: 1) a Muslim father from rural North India whose family is struggling with a clustering of risk factors, including poverty, the birth of a second daughter, a caesarean birth that adds cost to the hospital stay, and anti-Muslim prejudice; and 2) a Muslim father in impoverished rural Pakistan with his first son. The presenter, an exponent and practitioner of NBO, will discuss how each father finds a way to bond with his newborn and dream for the child’s future despite significant adversity. The videos demonstrate the powerful effect of the NBO, an intervention that engages strangers and the family’s community in the acknowledgement of a newborn, while also highlighting each individual father’s creative leadership in this experience. By focusing on the strengths and sensitivities of an individual baby and recognizing the infant as a competent little person, the principles of NBO become central to valuing girls as well as boys. Additionally, in communities with high infant mortality rates, the strength-based focus of NBO increases the motivation of the extended family and community to nurture the baby (as opposed to a baby initially being perceived as weak), thus increasing survival rates. We demonstrate the effectiveness of a range of practical strategies, including the NBO in facilitating infant-father bonding in Muslim families. These practices can be implemented in a home-based visit by a midwife or in a clinic setting.
The challenge of innovation and the nature of jobs under the pandemic-19

This article aims to discuss the impact of technological advances on the nature of work in Brazil, under three main aspects: a) the context of innovation processes; b) the introduction of industry 4.0 concept; c) the Covid-19 pandemic. Technological changes, both in the innovation process in the company, and in the advent of industry 4.0, were, to a certain extent, hastened by the emergence of the Covid-19 pandemic. In this sense, the analysis carried out by this study was based on data provided in surveys carried out by IBGE (Brazilian Institute of Geography and Statistics) and by IPEA (Institute of Applied Research). The findings of this research show two main aspects: a) workers in certain areas need to improve new skills, especially with regard to cognitive flexibility and emotional intelligence to support work in this new era of digitalization associated with remote work caused by the pandemic; b) companies will have to rethink their infrastructure and invest in new digital technologies to remain competitive and be able to operate with a portion of their employees working in the home office productively. In addition, they will have the flexibility to manage people in these conditions and obtain the best results from them.
Emotional Analysis with Deep Learning

– Image processing, one of the most exciting developments in deep learning, has made significantprogress in various fields today. Suspicious behavior detection, social media user sentiment analysis,customer feedback, marketing and sales strategies, and many other areas can now analyze human reactions.Studies on the analysis of facial expressions date back to the 19th century. In 1872, Darwin proposed theuniversality of facial expressions. Therefore, dealing with facial expressions is not a new concept. Basedon this, this project aims to integrate the concept of emotion analysis with the innovations of the modernera. According to a study conducted in 2012, Convolutional Neural Networks (CNN) are the mostsuccessful deep learning algorithm for object recognition. CNNs can distinguish features in images usingnumerous hidden layers. Filters that operate on matrices are used in each layer. These filters are the buildingblocks of the feature extraction process and play a crucial role in recognizing features such as edges, lines,colors, and other visual patterns. In this paper, a CNN model was built using the Python programminglanguage and the Keras library. The model was trained using the FER-2013 dataset, and images of facialexpressions were classified within the dataset. The performance of the model was evaluated using loss andvalidation metrics. Optimization efforts were made on the model to observe the changes in validationmetrics due to training parameters. With an optimized model based on this information, simultaneousemotion analysis, age estimation, and gender prediction can be performed.
Smart Wheelchair Design for Elderly and Disabled People

This paper proposes a gesture based wheelchair design using Raspberry Pi. A wheelchair isdesigned where physically disabled or elderly people can control the wheelchair and a nearby person cansee the status message on the LCD. These tasks can be performed using hand gestures. This enables thedisabled person to drive the wheelchair and move more efficiently from one place to another. The RaspberryPi acts as a brain that enables the components to fulfil their tasks. Flex sensors detect hand movements andare transferred as data input to the Raspberry Pi. The motor driver drives the motors. The system status isreflected as a message on the LCD screen and the operations are monitored. In return for four handmovements, the wheelchair can be controlled in forward, left and right directions and stop command. Ifdesired, the design can be edited and the motor movement and LCD screen message can be rearranged fordifferent hand movements. With the advantage of flexible design, personalized needs can be met. Thismakes the design more flexible and improvable than a joystick-controlled wheelchair and more costeffective than a voice-controlled wheelchair.
The Brain Drain Phenomenon in Developing Countries The Case of Albania

– In the globalization process, a phenomenon called "brain drain" is constantly in the center ofattention of countries' development policies. Developing countries are more widely affected by thisphenomenon, due to the negative consequences that can affect their economy. However, the first impact ofthis movement of human capital dates back to the 1960s, to describe the large-scale immigration of Britishscientists to the United States (Royal Society 1963). This article aims to analyze the negative impact that isgenerated on the economy of developing countries, in a worrying situation when two thirds of immigrantswith high skills come from developing and transition countries. The data show that, on average, the levelof brain drain that maximizes human capital accumulation in a developing country is about 10%. This levelvaries in different countries, depending on their size, country, language and public policies. The final resultsare expected to assess the fact that the brain drain brings an economic decline further reducing the welfareof the poorest nations. This phenomenon causes complex social problems that leave the countryintellectually and technologically underdeveloped, and further deepens the gap between developed anddeveloping countries.
A deep learning-based face recognition attendance system

Deep learning-based face recognition system have produced high accuracy and better performance when compared to other methods of face recognition like the eigen faces. Modern face recognition systems consist of different phases such as face detection, face alignment, feature extraction, face representation and face recognition. This paper proposes a deep learning approach in developing a face recognition-based class attendance system. The Multitask Convolutional Neural Network (MTCNN) is used for the face detection and alignment phase and a lightweight hybrid high performance Deepface Python framework based on the ‘Deepface’ Deep Convolutional Neural Network is employed for the feature extraction, face representation and face recognition phases with FaceNet-512 pretrained model. Because Convolutional Neural Networks (CNNs) perform better with larger datasets, image augmentation will be used on the original photos to enlarge the tiny dataset. The attendance record is stored in a MySQL database and accessed by an Application Programming Interface (API) developed using Hypertext Pre-processor (PHP) CodeIgniter framework. Cosine Similarity is used as the similarity metrics to compare the facial embeddings. A sliding camera system is deployed to aids the full coverage of the class participants irrespective of the size of the class. The test result show that all class participants were correctly identified and captured in the class attendance register generated.