Reviewerly/exHarmony huggingface dataset:
A PERT-BiLSTM-Att Model for Online Public Opinion Text Sentiment Analysis

As an essential category of public event management and control, sentiment analysis of online public opinion text plays a vital role in public opinion early warning, network rumor management, and netizens’ personality portraits under massive public opinion data. The traditional sentiment analysis model is not sensitive to the location information of words, it is difficult to solve the problem of polysemy, and the learning representation ability of long and short sentences is very different, which leads to the low accuracy of sentiment classification. This paper proposes a sentiment analysis model PERT-BiLSTM-Att for public opinion text based on the pre-training model of the disordered language model, bidirectional long-term and short-term memory network and attention mechanism. The model first uses the PERT model pre-trained from the lexical location information of a large amount of corpus to process the text data and obtain the dynamic feature representation of the text. Then the semantic features are input into BiLSTM to learn context sequence information and enhance the model’s ability to represent long sequences. Finally, the attention mechanism is used to focus on the words that contribute more to the overall emotional tendency to make up for the lack of short text representation ability of the traditional model, and then the classification results are output through the fully connected network. The experimental results show that the classification accuracy of the model on NLPCC14 and weibo_senti_100k public data sets reach 88.56% and 97.05%, respectively, and the accuracy reaches 95.95% on the data set MDC22 composed of Meituan, Dianping and Ctrip comment. It proves that the model has a good effect on sentiment analysis of online public opinion texts on different platforms. The experimental results on different datasets verify the model’s effectiveness in applying sentiment analysis of texts. At the same time, the model has a strong generalization ability and can achieve good results for sentiment analysis of datasets in different fields.
Design and Analysis of a Secure Hardware Implementation for Cryptography Applications (2020)

Networks need to communicate securely to ensure that data is kept safe from unauthorized access or malicious attacks.Cryptography algorithms play a key role in achieving this goal.Symmetric key algorithms use a single key to both encrypt and decrypt data, making them fast and efficient.However, the main challenge with symmetric key algorithms is key management, as the same key needs to be securely shared among all parties involved in the communication.On the other hand, asymmetric key algorithms use a pair of keys (a public key and a private key) to encrypt and decrypt data.The public key can be freely shared, while the private key must be kept secret.Asymmetric key algorithms are slower than symmetric key algorithms, but they offer a higher level of security.AES (Advanced Encryption Standard) is a symmetric key algorithm widely used for data encryption.It is considered to be very secure and is used by many organizations to protect sensitive data.RSA (Rivest-Shamir-Adleman) is an asymmetric key algorithm often used for key exchange and digital signatures.It is also considered to be secure, although it can be slower than other asymmetric key algorithms.By combining features of both symmetric and asymmetric key algorithms, it is possible to create a new security protocol that offers better security than using a single algorithm alone.The new protocol can leverage the speed and efficiency of symmetric key algorithms, while also benefiting from the increased security provided by asymmetric key algorithms.The main purpose of designing this new algorithm is to provide better security to data in transit against passive as well as active attacks.Passive attacks involve eavesdropping on the communication to gain access to sensitive data, while active attacks involve attempting to modify or disrupt the communication.By using a more secure protocol, organizations can protect their data from both types of attacks and ensure that it remains confidential, authentic, and available.
Courses reform of mould courses based on field augmented reality facing engineering education certification

Mould courses are crucial components of the mechanical manufacturing course system, and their innovation and reform can inspire new approaches to college education reform, as for the engineering education certification process. The augmented reality mould disassembly and assembly teaching scheme that is proposed in this paper can help students master the structure and processing of complicated moulds, and thus make comprehensive use of the relating learning knowledge and skills. By understanding of the typical structural design and assembly of moulds, students can learn actively by interaction of the three teaching strands of instruction, case study, and assessment. This will raise the level of instruction throughout the courses as well as student participation and interest. The proposed AR auxiliary system is a new approach to essentially foster students’ capacity for independent learning, knowledge understanding, and innovation and production in the age of digital learning and engineering education certification.
Zero-shot learning via visual-semantic aligned autoencoder

&lt;abstract&gt; &lt;p&gt;Zero-shot learning recognizes the unseen samples via the model learned from the seen class samples and semantic features. Due to the lack of information of unseen class samples in the training set, some researchers have proposed the method of generating unseen class samples by using generative models. However, the generated model is trained with the training set samples first, and then the unseen class samples are generated, which results in the features of the unseen class samples tending to be biased toward the seen class and may produce large deviations from the real unseen class samples. To tackle this problem, we use the autoencoder method to generate the unseen class samples and combine the semantic features of the unseen classes with the proposed new sample features to construct the loss function. The proposed method is validated on three datasets and showed good results.&lt;/p&gt; &lt;/abstract&gt;
Dual Level Security Scheme (DLSS) Using RGB Layer Cryptography and Audio Steganography for Secret Image Transmission in Unsecure Medium

Objectives: Recent trend depicts a steep hike in the value of data.Data in all forms are vulnerable and especially multimedia data are fascinated by hackers Hence, strong security measure has to be applied before attempting to use the unsecure transmission medium for sharing secret / personal data.Methods: Dual Level Security Scheme (DLSS) is proposed which combines cryptography and steganography techniques to ensure double level security.Cipher image is obtained by applying Spiral Mapping and discrete cosine transformations (DCT) in RGB Layers.Then, the cipher image is hidden in the audio signal.Findings: SNR value is considered for normalizing the signals before embedding it, to obtain final signal without any traces of the hidden secret signal.Core objective of using DLSS is maintaining the secrecy when data transmission is taking place in any unsafe medium.Simulation results of our proposed method exhibit an average of 10% improvement and hence, DLSS outperforms well over the existing methods.Competence of the method is evident from the performed histogram analysis.Standard image dataset is used and directional correlation, PSNR, SSIM, and MSE are the considered measures to prove DLSS efficiency.Novelty: Here, a novel method Dual Level Security Scheme (DLSS) with spiral mapping is proposed to incorporate the aids of cryptography and steganography for enhanced efficiency.
A novel management scheme for power grid demand response based on blockchain

The smart grid optimizes traditional power grids and provides more intelligent services for end users and utilities. However, due to the large number of unsafe devices in the smart grid, there are corresponding risks when implementing relevant solutions on these devices. Therefore, in this article, a security demand response management scheme based on blockchain is proposed to safely make energy trading decisions, thereby realizing the management of the overall load of users in the power grid. In this scheme, the miner node is the verifier. These nodes are responsible for verifying energy transactions in the smart grid (SG) and adding corresponding blocks to the blockchain. Successful energy transactions only occur in blocks in the blockchain. Here, the proposed method is validated through experiments on electric vehicles. The experimental results demonstrate the effectiveness of the proposed scheme in demand response management.
ULBC: An Ultra Light-weight Block Cipher

After explicitly observing the design criteria of two popular block ciphers, namely PRESENT and GIFT, we have proposed a new S-box that would be useful for designing a new light-weight block cipher, we name it as ULBC. The primary goal of the S-Box is to reduce the implementation cost, and make it cheaper than the two block ciphers GIFT and PRESENT. In this design, we have also developed a new property like BOGI which would be extremely helpful in building light-weight block ciphers. Depending on this property we can appropriately design permutation layer, such that no bad output will go bad input. ULBC is composed of a S-box, associated bit wiring and key schedule. Also, we have produced some of cryptanalytic attacks to assure certain security level. We have used two different methods to calculate differential characteristics and linear approximation. By using *-DDT method we have produced tight bounds of them. We find that 24 rounds are sufficient to resist some cryptanalytic attack. It is also found that by using this cipher64-bits plain-text can be encrypted into 64-bits cipher-text.
Design Thinking y redes sociales en la formación de futuros docentes: creatividad y comunicación digital

The rapid technological and cultural changes in today's society have inevitably affected the field of education. Future teachers must be prepared to meet the challenges of this changing society. An interesting tool for adapting to change and resolving conflicts is creativity. Moreover, in this society communication, especially in the digital environment, is essential. This paper shows the results of a project in which Design Thinking methodology and social networks are used to promote creativity and communication in students of degrees related to the educational world. To analyze the impact, a quantitative study was conducted by collecting student perceptions through a questionnaire. A total of 163 students from education degrees at the University of Jaén (Spain) participated in the study. The results show a change in their perception of their creativity and communication, along with other dimensions analyzed. Therefore, it is concluded that it is interesting to introduce both this methodology and social networks in teacher training as a preparation for the future educational challenges they will have to face in an increasingly digitized world.
ANALYSIS OF GLOBAL RESEARCH TRENDS IN THE INTERNET OF THINGS (IoT) AREA WITH NETWORK MAP TECHNIQUE

The Internet of Things (IoT) has emerged as a transformative technology paradigm that connects billions of devices, enabling seamless communication and data exchange. The study used the Web of Science Core Collection database to uncover global research trends in the IoT. The data in the study were visualized using the VOSviewer software, and bibliometric analysis was performed using the network map technique. The study aims to examine global research trends in IoT, examine IoT-related studies in social sciences, examine the status of IoT studies, especially in management and public administration, and guide researchers who aim to work on this subject. In the study, 87,397 studies on IoT between 2002 and 2022 were examined, and 1162 studies were included by limiting the studies to management and public administration. Studies were categorized and analyzed concerning fields of study, departments, years of publication, keywords, authors, and the most cited authors.
Error Analysis of Indonesian Speaker and Presenter for Practice of Public Speaking Class

This study aimed to reveal the Indonesian language errors found in the DA Public Speaking class. The data collection method used in this research was the observation method. The observation method is collected by recording, listening, and note-taking techniques. Descriptive qualitative method used as the data analysis in this research. The type of data used in this study was divided relating to Indonesian language errors that occur in the DA Public Speaking class. The research results found that participants in the DA Public Speaking class experienced various errors in their appearance in public speaking, either as speakers or presenters. Errors in analysis that often occur are word formation errors, dictions errors, pronunciation errors and sentence formation errors.
Research and application of YOLOv3-based intelligent module for remote sensing image recognition on satellites

With the continuous development of satellite technology, the acquisition and processing of satellite remote sensing data have become increasingly common. However, at present, satellites still use a mode of transmitting data back to the ground and utilizing ground CPU/GPU platforms for intelligent processing, which is inefficient due to the process of data transmission between the satellite and the ground. This article studies the application method and implementation of real-time remote sensing image recognition by integrating the Yulong810 on-board intelligent module on a satellite based on the YOLOv3 object detection algorithm. In response to the actual needs of satellite remote sensing image recognition, the algorithm is optimized based on the Yulong810 on-board intelligent module, which improves the recognition efficiency and reduces power consumption while ensuring the accuracy of remote sensing image recognition. By comparing with ground CPU/GPU platforms, the results show that the Yulong810 on-board intelligent module has lower power consumption and higher efficiency, and the recognition accuracy is comparable to that of ground platforms, which can completely replace ground remote sensing image detection equipment when integrated into satellites. By integrating the module into the satellite, real-time intelligent processing can be achieved in orbit, improving the efficiency of satellite remote sensing image recognition.
Students’ Perceptions of The Use of Japanese Language Learning Media Based on “QR Game” on The Implementation of The Independent Curriculum Class X at SMAN 7 Malang

In this digital era, technology should be utilized as much as possible to accommodate students' needs and learning styles.Using the descriptive quantitative method, this study aims to analyze students' perceptions of Japanese language learning with barcode scanner media as a learning tool for implementing fun learning.The sample of this study was class X at SMAN 7 Malang, and data collection was conducted using a closed questionnaire with a simple random sampling technique.The research steps include preparation (initial observation, literature study, and media making), implementation, and analysis.Based on the test results, it can be concluded that there is an increase in students' interest and understanding of Japanese language learning after the application of gamebased learning using barcode scanner media learning.
A Data-centric AI Framework for Automating Exploratory Data Analysis and Data Quality Tasks

Democratisation of machine learning (ML) has been an important theme in the research community for the last several years with notable progress made by the model-building community with automated machine learning models. However, data play a central role in building ML models and there is a need to focus on data-centric AI innovations. In this article, we first map the steps taken by data scientists for the data preparation phase and identify open areas and pain points via user interviews. We then propose a framework and four novel algorithms for exploratory data analysis and data quality for AI steps addressing the pain points from user interviews. We also validate our algorithms with open source datasets and show the effectiveness of our proposed methods. Next, we build a tool that automatically generates python code encompassing the above algorithms and study the usefulness of these algorithms via two user studies with data scientists. We observe from the first study results that the participants who used the tool were able to gain 2× productivity and 6% model improvement over the control group. The second study is performed in a more realistic environment to understand how the tool would be used in real-world scenarios. The results from this study are coherent with the first study and show an average of 30–50% of time savings that can be attributed to the tool.
An analysis method for improvement of coefficient consumption of related coefficients

Side channel analysis (SCA) is an emerging password analysis method for the password chip. The physical information leaked by monitoring the password algorithm on the physical chip will be obtained by processing the obtained signal to extract the sensitive information in the encrypted device through processing analysis. In recent years, the rapid development of power consumption analysis methods in SCA has become a powerful side channel attack method. Therefore, the ability to protect password equipment from SCA's infringement has gradually become the focus of the information security industry. This article first elaborates the research status of the field of side channel analysis, introduces many problems in the field of side channel analysis, and proposes an improved power consumption analysis method for power analysis attacks under the condition of less power trace analysis. This method has successfully reduced the number of power trace points required by the recovery of the correct key in the AES-128 encryption algorithm encryption, and can still ensure a higher classification accuracy in the case of a small amount of power traces.
Multi-Modal Learning-Based Blind Video Quality Assessment Metric for Synthesized Views

The quality attenuation of synthesized video will directly affect the widespread adoption of immersive video, so it is crucial to design a quality assessment model that can determine whether the synthesized video meets the requirements of commercial broadcasting. However, designing a general-purpose no-reference quality assessment metric for synthesized videos is difficult due to the imperfect view synthesizing technology and scene diversity. Currently, the existed quality assessment algorithms for synthesized views are mostly based on handcrafted feature extraction. Inspired by the theory that the input stimuli are hierarchically and sparsely processed in the cerebral cortex, we combine Convolutional Neural Network (CNN) learning and sparse dictionary learning mechanisms, and propose a Multi-Model Learning based Blind Synthesized Video Quality Assessment (MML-BSVQA) metric. Firstly, to better reflect the spatio-temporal distortions, we convert the synthesized video into the Spatial Domain (SD), Vertical Temporal Domain (VTD) and Horizontal Temporal Domain (HTD) using video decomposition operation plus optical flow estimation. Secondly, we extract the deep semantic features from three domains based on a pre-trained CNN model. Thirdly, we represent the sparse features of three domains using respective trained over-complete sparse dictionaries. Note that both the CNN model and sparse dictionaries are trained on natural videos to ensure the general-purpose of the proposed MML-BSVQA metric. Finally, the score of a synthesized video is generated by weighted regression. Experimental results on three synthesized video databases demonstrate that the proposed metric outperforms classic and state-of-the-art quality assessment metrics.
A calibration method for line-structured light using mirror-based virtual binocular vision system

Abstract A calibration method for line-structured light (LSL) by using a virtual binocular vision system (VBVS) composed of one camera and a front coating plane mirror is promoted in this work. The front coating plane in the VBVS can generate much less coplanarity error in lithographic feature points and remarkably decline the imaging distortion during back coating. An encoded target is proposed to distinguish between real corners and virtual corners (mirrored corners) and achieve high-precision matching between real and virtual corners when the target is occluded during the VBVS calibration. A parameter optimization method based on 3D constraints is presented in the work to obtain accurate structural parameters and thus guarantee precise reconstruction of the LSL. Moreover, the laser stripe and its mirrored image meet the auto-epipolar constraint. Therefore, the matching between the real and virtual stripes can be realized based on the vanish point. The performance of our method is verified in the experiments.
Gestures detection and device control in AAL environments using machine learning and BLEs

This paper presents a system for detecting gestures and controlling devices in Ambient Assisted Living (AAL) environments using machine learning and Bluetooth Low Energy (BLE) technology. The system consists of two main components: a device equipped with a set of sensors to detect hand gestures via IMU sensor and a BLE-enabled hub that receives the gesture data and controls the lighting of the house. The hub uses machine learning algorithms to recognize hand gestures and transmit the corresponding commands to the devices. The hub, in turn, uses wifi to communicate with the devices and execute the appropriate actions based on the received commands. The proposed system's performance evaluation was carried out through a series of experiments in a AAL environment. The results demonstrate that the system is capable of accurately detecting hand gestures and controlling various devices such as lights, where the model's performance yields successful predictions with an accuracy rate of 90%. The proposed system provides a user-friendly and intuitive way for elderly or people with disabilities to control their environment without the need for complex interfaces or physical buttons. Furthermore, the system can be easily extended to support more gestures and devices, making it a flexible and scalable solution for AAL environments.