Reviewerly/exHarmony huggingface dataset:
Bridge Architecture for Medical Question Answering with Negative Sample Training

The pre-trained model can achieve good results on a variety of natural language processing tasks which is a breakthrough in the field of natural language processing. However, the pre-trained model is trained on a task-independent corpus, there will be a certain gap between the pre-trained model and downstream tasks. Here we propose a novel bridge architecture to bridge the gap between the pre-trained model and the medical question-answering task. This method does not require a lot of computing resources to fine-tune the pre-trained model, nor does it require specific prompts for downstream tasks. The effectiveness of our architecture is verified by comparison with the strong baseline model. At the same time, we innovatively propose a question answering model training method based on negative sampling with infoNCE loss. Using this training method could improve the effectiveness of the model while greatly reducing the time consumption of model training.
Trans-CNN: A Sentiment Analysis Model Which Focuses on Key Information of Sentence

Most of sentiment analysis methods rely on large-scale pre-trained model, which require a lot of computing resources.Moreover, the current model only extracts features on the sentence level, which cannot consider the influence of sentence meaning or local keywords on emotional polarity.In order to solve these problems, we use multi-head attention,recurrent neural network(RNN) and convolutional neural network(CNN), by paying attention to the global and local feature information of sentences, and enhancing the expression of emotional features of sentences. Without the help of pre-trained model, we propose Trans-CNN.Firstly we get the vector representation of the sentences. Then with the help of multi-head attention mechanism and CNN, we focus on the global and local feature information of sentences. Finally the feature information is fused and the softmax classifier is used to get the emotional label of the text. Experiments show that the proposed method has better performance on IMDB data set. Compared with models without pre-trained method,our model achieves better performance.
3D Adaptive SMCM Filtering of CT Images

In this paper a new implementation of the Saint-Marc-Chen-Medioni (SMCM) filter is proposed in a 3D variant. It uses adaptive selection of the threshold <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> , found from a 2D projection of the input image. The algorithm has been tested over Computed Tomography (CT) images with Additive White Gaussian Noise (AWGN) at variance levels 0.01 and 0.001. It leads to better quality of filtered images in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM), compared to 3D Gaussian and 3D Averaging filters. The proposed 3D SMCM filter is considered applicable for filtering of other types of 3D images, such as Magnetic Resonance Images (MRI).
Image Denoising Via Multi-Task Learning

Although convolutional neural networks (CNN) have notably improved the effect of image denoising, removal of non-Gaussian noise remain a challenging problem. In this work, the statistical characteristic of image residuals is investigated and used as auxiliary information for better removing complex type noise via multi-task learning method. We propose an improved algorithm for denoising CNN (DCNN) by optimizing the training of the DCNN and it can achieve a Pareto optimal solution. Extensive experiments on benchmark data sets with different noise models demonstrate that the proposed method can effectively improve the quality of denoised images both in Gaussian and non-Gaussian noise, even when the network architecture is left unchanged.
Anti-Amnesia: Developing a Collaborative e-learning and Digital Archive Platform Towards Contributing to the Preservation and Revitalization of Handicrafts Industries

The study presented in this paper consists of the development of a platform for digital archiving, collaborative and e-learning, within the framework of the craft industries. This work is part of Anti-Amnesia, a project which aims to investigate Design as an agent for the regeneration and reinvention, narratives and materials, of disappearing Portuguese cultures and manufacturing techniques. In general, this project aims to: develop methodologies, through digital means and tools, that contribute to the preservation of the heritage and cultural heritage of the craft industries; contribute to the sustainability of the craft heritage, through the revitalization of the tradition of these industries, ensuring a continuous transmission of skills, techniques and craft knowledge to future generations; and promote research and research in this area of Portuguese manufacturing. Specifically, in this work to create the platform, we seek, through digital technology, to make information more accessible and universal, facilitating access to knowledge. Through the platform, and with the support of educational tools and services, we will seek to promote the learning of Portuguese manufacturing techniques, in order to attract young people to recover older practices. Based on this strategy, it is intended to value tradition, preserve it, and transmit it to future generations.
Image Brightness Adjustment with Unpaired Training

In camera-based imaging, exposure correction is an important issue. Wrong exposures include overexposure and underexposure, while prior works mainly focus on underexposed images or general image enhancement. However, it is challenging to collect images of different brightness levels in practice. This paper proposes a network structure based on Generative Adversarial Network (GAN) trained on unpaired image datasets. Instead of using ground truth data to supervise learning, we adopt information extracted from the input to regular unpaired training. Our method uses style transfer for reference to separate the content space from the brightness space, realizing the conversion of an image of arbitrary brightness into images of different brightness. A large number of experiments demonstrate that our method realizes the conversion of different brightness. Our work can restore the overexposed and underexposed images of different levels and enhance low-illuminance images effectively. We organize a dataset with different exposure levels based on the existing dataset as our training dataset.
Using K-Nearest Neighbor Algorithm for Personality Classification of Twitter’s Users Based on the Big Five Theory

Social media is an application or website-based system that enables users to create and share content or participate in social networking that allows its user to share their thoughts, opinions, or feelings that represent their personality. At present several studies to classify an individual's personality through social media have been developed, especially on social media Twitter. However, most of the analysis on Twitter only uses text based data such as posted tweets. This research presents a study on analyzing the users’ twitter data to classify their types of personality based on Big Five Theory by using their social statistic data. The data were acquired using Twitter API which was taken from Indonesian users with the total of 225 data. This study shows that using K-Nearest Neighbor (K-NN) Algorithm for classification of these data were not resulting in high accuracy. However, this study has shown that amount and balance distribution of training data critically contribute to the performance of classification process.
Security and Threats in the Brazilian e-Voting System: A Documentary Case Study Based on Public Security Tests

Democracy is one of the processes that has become electronic over the years, and Brazil, as one of the countries with the largest democracy in the world in terms of number of voters, has also started the informatization of the voting process. However, it is important to note that, in addition to advantages that an all-electronic voting process brings to an election, such as rapid vote tabulation and the availability of results, there are technical issues to be addressed to prevent fraud and system failures, ensuring a fair process. In this sense, this paper presents a case study that analyzes what are the problems faced in the Brazilian electronic process by studying public reports released by the authorities. Brazilian e-voting system has several security mechanisms, such as voter authentication by biometrics, and is capable of detecting unauthorized modifications. Our findings show that, despite the Brazilian e-voting technological evolution, the system still faces some problems that can compromise the outcome of an election, and also bring some doubts about the procedures defined for carrying out public security tests in the e-voting system.
3D U-Net for Video Anomaly Detection

With the widespread of surveillance video application, anomaly detection in surveillance video is significant for safety maintenance. In this paper, we propose a new abnormal detection framework, which contains an improved future frame prediction model based on GAN network to locate the abnormal events in the video. Unlike the previous U-Net network which is usually used as prediction model in abnormal event detection, 3D U-Net is adopted in our proposed network for predicting future frames which can fuse the spatial and temporal features simultaneously by using 3-dimensional convolution. For normal events, the improved prediction network takes into account the temporal features, resulting in low abnormal scores, while abnormal events have high abnormal scores. Experiments show that this method can get good results on the Avenue, UCSD Ped2 and ShanghaiTech datasets, and the AUC values on these datasets could reach 86.0%, 96.3% and 73.6% respectively.
ChaDRaL: RGB Image Encryption based on 3D Chaotic Map, DNA, RSA and LSB

ChaDRaL, an RGB image encryption process built on Chaos, DNA, RSA and LSB is introduced in this work. ChaDRaL leverages the advantages of symmetric and asymmetric cryptography as well as steganography. On one hand, the image is first encrypted with a symmetric algorithm combining DNA sequence operations and Lorenz chaotic system. On the other hand, the encryption key which is utilized to encrypt the media is now itself encrypted with an asymmetric algorithm i.e. RSA. Lastly, this encrypted key is concealed in the cipher image using LSB steganographic scheme. As a result, the problem of key transfer is also eliminated. A variety of test images are utilized to put the ChaDRaL algorithm to test. The suggested method’s security analysis reveals little correlation among image pixels, high entropy, and uniform distribution in histogram of cipher image. Also, it shows considerable performance in terms of established metrics such as UACI, NPCR, PSNR and SSIM. The simulation results demonstrate the capability of ChaDRaL to achieve quality encryption while resisting statistical and differential attacks.
Dynamic Network Pruning Based on Local Channel-Wise Relevance

In recent years, deep convolutional neural networks (CNNs) significantly boost the various applications, but the high computational complexity of these models hinder the further deployment on device with limited computation resources. Hence, dynamic channel pruning has been recently proposed and widely used for compressing CNN-based models. In this paper, we propose a novel plug-and-play dynamic network pruning module. With very slight extra computation burden, it can achieve the comparable performance as the original model. Specifically, our proposed module measures the importance of each convolutional channel to prune the CNNs with small decrease in accuracy. The module reduces the computation cost by global pooling and channel-wise 1-dimensional convolution that considers the channels’ locality. Comprehensive experimental results demonstrate the effectiveness of our module, which makes a better trade-off between the performance and the acquired computational resources, comparing to its competing methods. In concrete, our dynamic pruning module can reduce 51.1 $$\%$$ FLOPs of VGG16 with only 0.18 $$\%$$ top-1 accuracy degradation on CIFAR10.
Learning System Based on AppCan Platform

With the development of mobile Internet, mobile application has become an indispensable part of mobile intelligent terminal. It is an important direction of mobile applications to make full use of fragment time to meet the actual needs. Based on the actual application requirements, this paper designs a cross-platform mobile English learning system based on AppCan, which has rich functions and realizes cross-platform use on mainstream systems such as Android and iOS. The user experience shows that the system has achieved the design goal.
Linear, Logistic, and Kernel Regression

In machine learning, regression analysis refers to a process for estimating the relationships between dependent variables and independent variables. This method is mainly used to predict and find the cause-and-effect relationship between variables. For example, in a linear regression, a researcher tries to find the line that best fits the data according to a certain mathematical criterion (see Fig. 3.1a).
High capacity coverless image steganography method based on geometrically robust and chaotic encrypted image moment feature

In recent years, coverless image steganography attracts significant attentions due to its distortion-free trait on carrier images to avoid the detection by steganalysis tools. Despite this advantage, current coverless methods face several challenges, e.g., vulnerability to geometrical attacks and low hidden capacity. In this paper, we propose a novel coverless steganography algorithm based on chaotic encrypted dual radial harmonic Fourier moments (DRHFM) to tackle the challenges. In specific, we build mappings between the extracted DRHFM features and secret messages. These features are robust to various of attacks, especially to geometrical attacks. We further deploy the DRHFM parameters to adjust the feature length, thus ensuring the high hidden capacity. Moreover, we introduce a chaos encryption algorithm to enhance the security of the mapping features. The experimental results demonstrate that our proposed scheme outperforms the state-of-the-art coverless steganography based on image mapping in terms of robustness and hidden capacity.
ONLINE LEARNING QUESTIONNAIRES BASED ON RENEWABLE ENERGY RESEARCH EQUIPMENT

This paper resumes the main ideas to develop different sets of questionnaires based on renewable energy research equipment to improve online learning in engineering education. These educational activities, on the one hand, promote the connection between technological and education environments and, on the other hand, improve the motivation for studying theoretical and practical issues. The implementation of the proposed teaching and learning tasks will contribute to integrate students with different motivation and attitudes since the use of the close physical equipment for questionnaires development has an evident educational benefit when using it as a study model. The evolution of technological environments to educational units involves a functional Disassembly/Analyze/Assembly conversion to different modules adapted to specific subjects, the link with the theoretical knowledge, and finally, the questionnaire construction. The combination proposed offers the following positive results in master or grade studies: the effectiveness of competences training is improved, the students experimental work progresses significantly and the students’ motivation for better theory comprehension is more intensively developed. The use of these kind of questionnaires as educational tools is not new and their advantages are well known: the quick feedback results and the systematic data collection to support the educational proposal effectiveness, among others. Nevertheless, this tool has become a necessity with the massive use of online education. In summary, promoting the evolution of engineering education to online techniques and the use of renewable energy research equipment to promote the students’ vision on social necessities are two examples of educational goals stated in this paper.
Mobile Robot Navigation with Preference-Based Fuzzy Behaviors

A behavior based fuzzy control for two-wheeled mobile robot with navigation is considered. Solving the problem of avoiding obstacles inevitably becomes an important task of robotics in case of impossibility to calculate the trajectory before the start of its movement, or unforeseen changes in the working environment, or in the absence of information on the exact location of obstacles. A problem can be interpreted as a requirement for robot`s movement from a starting point to the given target avoiding the obstacles, reaching the target in the shortest time, avoiding trapping. Getting from an initial position and cruising to the next position is under closed-loop control. A mobile robot should stop at the target within a very small position error. For coordination of these behaviors a multi-objective techniques was applied. A selection from a set of behaviors was represented as vector optimization problem. Behaviors are selected as Pareto optimal solutions, using lexicographic method.
A Feature Fusion Method Based on Multi-Classification Losses for Fine-Grained Visual Categorization

Existing approaches based on the SGR (single granularity recognition) improve the recognition accuracy by expanding the dataset, learning more robust feature representation or locating and amplifying significant parts. These approaches often suffer from heavy computational costs, high model complexity or additional non-classsification losses. In this paper, we propose a feature fusion method based on multi-classification losses, which can classify different granularity features and make the network focus on more useful information. The complexity and computational costs of our model have only increased slightly, which can be ignored. The results of comparative experiments show the effectiveness of our proposal on Stanford Cars dataset.
Comparative Sharpness Evaluation for Mobile Phone Photos

Mobile phones are the main source of a vast majority of digital photos nowadays. Photos taken by current mobile phones generally have fairly good visual quality without noticeable distortion. This progress benefits not only from the higher specification of camera sensor, but also the excellent imaging algorithm. Reliable and practical photo quality evaluation algorithm can guide the development of mobile phone photography to a higher level, which is also the key to this progress. Clearly, subjective photo quality assessment suffers from the drawbacks of productivity and reproducibility. Traditional objective image quality assessment algorithms can hardly discern the subtle quality difference between pictures photoed by different mobile phones. In this paper, we propose a comparative sharpness evaluation method based on an improved Siamese network with multilayer features for mobile phone photos. We employ Resnet-18 as the main feature extraction module. The features extracted from different layers of the two branches will be concatenated layer by layer and then passed to the final fully connected layer. The final output represents the sharpness comparison result of the two input pictures. Experimental results show that our sharpness evaluation algorithm achieves state-of-the-art performance on the SCPQD2020 database.
Self play with parameter sharing in n-player mixed competitive-cooperative games

We introduce some parameter sharing multi-agent reinforcement learning schemes, combined with self-play for n-players mixed competitive-cooperative games. Except for the pure self-play scheme, an another one using the best policy, outperform the pure parameter sharing baseline, leading to better exploration of the state space and protecting from the performance deterioration observed with the baseline.
Stylized Dialogue Generation

Dialogue systems such as intelligent online customer services, online chatbots or smart kiosks are becoming increasingly popular. Currently dialogue systems lack personality and ability to respond according to contexts. In this study, we propose an approach to transfer the text into multiple styles when generating dialogue responses. It is especially challenging to build a stylized dialogue system as it combines supervised and unsupervised tasks. In practice, the dialogue data are usually paired, i.e. query paired response while styled text is not. Therefore, we propose using lightweight deep neural network models to bridge the dialogue response generation model and the style transfer model. This structure allows the model to generate responses of different styles to the same query. Our approach will be evaluated against selected state-of-the-art dialogue generation and style transfer techniques.
SCORER-Gap: Sequentially Correlated Rules for Event Recommendation Considering Gap Size

Finding significant events, which follow a specific pattern, is an essential task in sequential rule mining. While the significance of a rule often is based on conditions like a maximum amount of time [1], or a minimum distance between patterns [2], the area between these two extremes is rarely analyzed. This paper aims at the discovery of partially-ordered sequential rules which satisfy a given correlation gap constraint. Applying this constraint to the support threshold determines a more relevant rule, among other parameters. We also require it in sparse datasets, where long sequences with many distinct events exist. This setting can be found in online product configurators, where the basis is an unstructured process that combines both high-level and fine-grained configuration steps. In general, our novel approach SCORER-Gap can be applied to procedures with a high variability of events.By focusing on the gap size between antecedent and consequent of a rule, we show that usually, the resulting vast number of rules gets highly reduced while keeping the flexibility between a minimum and a maximum distance in between. To implement our novel approach, we use an in-mining setup, namely RuleGrowth [1] to which we attach the correlation gap constraint as mentioned above. The code is available on [3]. For an extensive analysis of application areas, we use three real-world datasets consisting of different characteristics. We start with a Covid19 genome sequence representing a highly dense dataset. Additionally, an industrial database and the clickstream of a Hungarian news website (Kosarak) are used as representatives for increasingly sparse datasets.SCORER-Gap shows a high percentual reduction in the number of rules in the resulting ruleset while slightly increasing accuracy in a train and test setting. Furthermore, a high proportion of recommendation rules differs between RuleGrowth and SCORER-Gap.
An Energy Trading Privacy Protection Scheme Based on Blockchain and Non-interactive Zero-Knowledge Proof

With the marketization reform of energy trading, distributed energy trading becomes more and more important. The decentralized characteristics of blockchain can be well combined with energy trading to realize point-to-point transactions between users and distributed prosumers, and the open and transparent characteristics of blockchain can also make the legitimacy of transactions be notarized by the whole network nodes. But at the same time, the energy trading information belongs to the user’s private information, so the information on the blockchain needs to be protected for privacy. This paper proposes a non-interactive zero-knowledge proof privacy protection scheme based on pederson commitment and rangeproof. Pederson commitment has the property of homomorphic addition, which can well hide users’ energy trading data, and rangeproof can provide proof for the legitimacy of energy transaction. The scheme proposed in this paper can enable users to conduct distributed energy transactions, and the legitimacy of the transactions can be verified by the nodes of the whole network, and at the same time protect the privacy of users.