Reviewerly/exHarmony huggingface dataset:
Image Quality Improvement for Capsule Endoscopy Based on Compressed Sensing with K-SVD Dictionary Learning

Reducing the power consumption of capsule endoscopy is essential for its further development. We introduce K-SVD dictionary learning to design a dictionary for sparse coding, and improve reconstruction accuracy of capsule endoscopic images captured using compressed sensing. At a compression ratio of 20%, the proposed method improves image quality by approximately 4.4 dB for the peak signal-to-noise ratio.
XReC: Towards a Generic Module-Based Framework for Explainable Recommendation Based on Character

The ongoing improvements of technology worldwide helped humans and businesses in different aspects by enhancing human-computer interactions. Especially after the outbreak of the COVID-19, people head to the virtual world by shopping online instead of going to the actual store, watching movies on platforms like “Netflix” instead of going to cinemas, or companies are applying different methods to continue their internal operations online. So most companies now invest much effort to enhance their online platforms to cope with the occurring situation. One way of enhancing the online system, especially in E-commerce, E-learning, and entertainment platforms, is by building robust recommendation algorithms. Recommender systems play a massive role in improving the online experience by suggesting to the user relevant items. However, treating all users the same by applying recommendation strategies that do not include the user her-/himself as the center of the algorithm may lead to an unpleasant user experience as each user has a different personality, taste, and different needs. Thus, in this paper, a structured review of the efforts invested in creating personalized recommendation systems is studied to explore the personal factors included in previous trials. Accordingly, we propose XReC, a generic framework for building character-based recommender systems based on Character Computing principles, including all the aspects that influence human behavior. As a result of integrating multiple human aspects, the system’s complexity will arise. A personalized explanation accompanying each recommendation is provided to improve our framework’s transparency, trustworthiness, persuasiveness, effectiveness, and user satisfaction.
The application of the BYOD (Bring Your Own Device) concept in industry and education: Implications for a change of university practice

With the development of mobile technology emerge fundamental changes in all spheres of human endeavor. In education, new methods of remote studying are being developed, with a particular emphasis on "m-learning" (learning with the help of mobile devices). Taking into consideration that mobile devices are one of the fastest-developing forms of technology, the importance of their assistance in the process of teaching and studying has been recognized. The goal of this paper is to introduce the concept of Bring Your Own Device (BYOD) in the context of industry and education as an example of disruptive technology. This concept implies that students bring their own mobile devices to the classroom and use them when learning. Using a descriptive research method and analysis of the relevant bibliography, the authors of the paper distinguish the implications for the change of university practices on the basis of critical analysis of positive and negative consequences of their use, redefined positions of the teachers and the students in the educational process, as well as the modified educational design.
On the Cayleyness of Praeger-Xu graphs

This paper discusses a family of graphs, called Praeger-Xu graphs and denoted PX(n,k) here, introduced by C.E. Praeger and M.-Y. Xu in 1989. These tetravalent graphs are distinguished by having large symmetry groups; their vertex-stabilizers can be arbitrarily larger than the number of vertices in the graph. This paper does the following: (1) exhibits a connection between vertex-transitive groups of symmetries in a Praeger-Xu graph and certain linear codes, (2) characterizes those linear codes, (3) characterizes Praeger-Xu graphs PX(n,k) which are Cayley, (4) shows that every PX(n,k) is quasi-Cayley, and (5) constructs an infinite family of Praeger-Xu graphs in which a smallest vertex-transitive group of symmetries has arbitrarily large vertex-stabiliser.
On the relation between technical debt indicators and quality criteria in Stack Overflow discussions

Context: Technical debt (TD) can compromise the quality of software systems in the long term. However, depending on the needs and properties of the specific product, different quality characteristics may be considered in the assessment with different levels of relevance. In this way, quality characteristics can guide teams in managing the TD, from identifying to paying for the TD items relevant to each situation. Although a set of TD item indicators has been identified in the literature, there is no clear guidance on how these indicators can be efficiently used to identify TD items. Aims: To investigate, from the point of view of software professionals using the SO platform, the relationship between the TD item indicators and the quality characteristics of the system, in order to guide the use of indicators for greater efficiency and effectiveness in identifying and managing DT items from a quality perspective. Method: We extracted empirical evidence using data mining from discussions related to TD on the SO. From the execution of an analysis process, the quality characteristics related to the occurrence of DT items identified in the discussions, the respective indicators and types of debt were identified. Results: The main results reveal that maintainability is the characteristic most related to TD through its sub-characteristics: modifiability, testability and modularity. Code debt items can be related to all quality characteristics, while Infrastructure debt can resonate up to 6 characteristics. All quality characteristics have at least one associated indicator, with an emphasis on Maintainability with 13 high-level and 28 low-level indicators. The indicator related to version problems is the one with the highest number of quality features. Conclusion: The relationship between indicators, TD types and quality characteristics was organized in a conceptual diagram in order to assist software teams in identifying which TD items are related to the quality characteristics that are critical to the project context, contributing to decision making regarding the best strategy for debt management.
The Bisq decentralised exchange: on the privacy cost of participation

The Bisq Trade Protocol and the Bisq DAO (decentralised autonomous organisation) are core components of Bisq, a decentralised cryptocurrency exchange. The Bisq Trade Protocol systematises the peer-to-peer trading of Bitcoin for other currencies and the Bisq DAO decentralises the governance and finance functions of the entire exchange. However, by following the Bisq Trade Protocol and interacting with the Bisq DAO, participants necessarily publish data to the Bitcoin blockchain and broadcast additional data to the Bisq peer-to-peer network. We examine the privacy cost to participants in sharing this data. Specifically, we use novel address clustering heuristics to construct the one-to-many mappings from participants to addresses on the Bitcoin blockchain and augment the address clusters with data stored within the Bisq peer-to-peer network. We describe address clustering heuristics for both the Bisq Trade Protocol and the Bisq DAO. We show that the heuristics aggregate activity performed by each participant: trading, voting, transfers, etc. We identify instances where participants are operating under multiple aliases, some of which are real-world names. We identify the dominant transactors and their role in a two-sided market. We conclude with suggestions to better protect the privacy of participants in the future.
Consensus Feature Network for Scene Parsing

Scene parsing is challenging as it aims to assign one of the semantic categories to each pixel in scene images. Thus, pixel-level features are desired for scene parsing. However, classification networks are dominated by the discriminative portion, so directly applying classification networks to scene parsing will result in inconsistent parsing predictions within one instance and among instances of the same category. To address this problem, we propose two transform units to learn pixel-level consensus features. One is an Instance Consensus Transform (ICT) unit to learn the instance-level consensus features by aggregating features within the same instance. The other is a Category Consensus Transform (CCT) unit to pursue category-level consensus features through keeping the consensus of features among instances of the same category in scene images. The proposed ICT and CCT units are lightweight, data-driven and end-to-end trainable. The features learned by the two units are more coherent in both instance-level and category-level. Furthermore, we present the Consensus Feature Network (CFNet) based on the proposed ICT and CCT units, and demonstrate the effectiveness of each component in our method by performing extensive ablation experiments. Finally, our proposed CFNet achieves competitive performance on four datasets, including Cityscapes, Pascal Context, CamVid, and COCO Stuff.
Leveraging Few-shot learning architectures to tackle limited data model training

With privacy protection schemes hailing stronger than ever before, it is becoming virtually impossible to train Machine Learning (ML) models. Hence, it is highly required to find optimal ways to train ML models even with low amounts of data. While Federated Learning is a great way to deal with this, it has extremely high requirements in terms of device bandwidth, processing power, and memory storage. This paper proposes two Few-Shot learning architectures to tackle these issues. Further, these operations are performed on NLP-based services in federated architectures.
Underwater Image Enhancement Method Based on Wavelet Transform and Retinex

Due to the complex underwater environment, the light will be scattered and absorbed in water, so the underwater image has problems such as poor contrast, blur, uneven illumination and so on. An underwater image enhancement method based on wavelet transform and Retinex algorithm is proposed. The algorithm consists of three main steps. Firstly, the image is converted into HSI color space, and the brightness component is preprocessed by Retinex algorithm. Then, the preprocessed brightness component was transformed by wavelet transform, and the low-frequency component was enhanced by contrast limited adaptive histogram equalization (CLAHE), and the high-frequency component was enhanced by logarithmic function. Finally, the enhanced image is obtained by inverse wavelet transform. The peak signal to noise ratio (PSNR) and structural similarity index (SSIM) were used as objective indexes to evaluate the enhanced underwater image, and the experimental results show that the algorithm can improve the quality of underwater image very well.
Nighttime vehicle detection on highway based on improved faster R-CNN model

Vehicle monitoring using cameras is an important task for highway management. At present, most vehicle detection algorithms are applied in daytime. However, compared with the daytime, the road environment at night is darker and the vehicle body is not obvious, the existing algorithm is difficult to meet the vehicle detection requirements in the night scene. Due to the complexity of night scenes, video images bluring and excessive noise, vehicle detection at night poses a great challenge. Aiming at the above problems, this paper proposes a nighttime vehicle detection framework based on improved Fasterr R-CNN. Firstly, the deep residual network is used to extract features autonomously, and the spatial attention mechanism is integrated to make the network pay more attention to the road region rather than the background region. Secondly, balanced feature module is introduced to make full use of the extracted visual features. Finally, Soft- NMS replaces NMS to reduce the number of missed-detection vehicles. Experimental results show that the AP value of the improved Faster R-CNN model is 4.18% higher than that of baseline Faster R-CNN model.
Systematic Literature Review of Methods for Maintaining Data Integrity

Diverse data is collected in health care for secondary usage. However, this data is collected by various parties and processed by numerous information systems. Hence, to get reliable results, the data integrity must be maintained. This paper aims to discover applications that are introduced to ensure the integrity of the data collected in different fields including the medical field. The Systematic Literature Review method is used in this research to map the appropriate methods for future studies.
IOU-Guided Siamese Tracking

Target tracking is currently a hot research topic in machine vision. The traditional target tracking algorithm based on the generative model selects target features manually, which has a simple structure and fast running speed, but it cannot meet the requirements of algorithm accuracy in complex scenes. Compared with traditional algorithms, due to the good performance, the tracking method based on full convolutional network has become one of the important methods of target tracking. However, the RPN-based Siamese network lacks positional reliability when predicting the target area. Aiming at the low tracking accuracy of the RPN-based Siamese network, this paper proposes an improved framework model named IoU-guided SiamRPN (IG-SiamRPN). In the proposed IG-SiamRPN, the IoU-guided branch is first constructed and sample pairs are generated through data augmentation. Then, the Jittered RoI is constructed to train the network to realize the direct prediction of the localization confidence of the candidate area. Subsequently, a target selection method based on predicted IoU scores is proposed, which uses predicted IoU scores instead of classification scores to optimize the target decision strategy of the Siamese network. Finally, an optimization-based fine-tuning method for the Siamese network frame is proposed, which solves the problem of location degradation and improves the performance of the algorithm. Compared with other state-of-the-art target tracking algorithms, experimental results on popular databases demonstrate that the proposed IG-SiamRPN can achieve better performance in both tracking accuracy and robustness.
A fair classifier chain for multi‐label bank marketing strategy classification

Abstract Recently, the usage of machine learning algorithms is subject to discussion from a legal and ethical point of view. Unwanted discrimination regarding gender or race of a prediction model can lead to legal consequences. Therefore, during predictive model learning, one needs to be aware of possible bias and adjust the model to be fair. However, in bank marketing applications, one customer can receive multiple offers instead of just one. Because of their correlation between, a multi‐label classification approach is the most suitable one. This paper proposes a fair classifier chain machine learning model for multi‐label classification. Our algorithm solves the multi‐label classification problem in an efficient manner, and it is suitable for real‐life application employment. The proposed approach allows for controlling fairness constraints during the process of machine learning. It is based on the logistic regression model, thus enabling high efficiency and understandability. We apply our model to a real‐life model from bank marketing campaign response prediction. The obtained results are promising. More specifically, our model achieves high fairness measures having an increase from 7% to 17%. However, fairness has a price of a decrease in predictive performance, up to 9% of AUC. To the best of our knowledge, this is the first algorithm that introduce fairness constraints in multi‐label classification problems.
Emotion Extraction from Text Using Fuzzy-Deep Neural Network

Emotions are one of the most important and affective start points of our decisions in daily life. People express their emotions, consciously or unconsciously, in their behaviors, like speech, facial expressions, textual content, and so on, whether it be in blogs, reviews, or on social media. Due to the rapid growth in textual social media to link people, the emotion extraction from textual data has attracted a lot of attention. This paper performs the sentence-level discrete emotion extraction from text by using Fuzzy Neural Networks (FNN) and Deep Recurrent Neural Networks (DRNN). Much work has been presented to extracting emotion from textual data with precise and constant boundaries, but little of it has considered the inherent uncertainty in natural language. In other words, the high ambiguity of emotion in text data makes an emotionally sentence express multiple emotions at the same time. To deal with this uncertainty we use a Neuro Neural Network (NNN). But from another point of view, by increasing the number of inputs, the number of parameters in NNN growth exponentially. To this end, we extract a low-dimensional semantic representation of input sentence by using a Bi-directional Long Short-Term Memory (Bi-LSTM). Our goal is to achieve better performance with combining the advantages of deep learning in high-level semantic feature extraction to ambiguity handling and the capabilities of fuzzy logic with fuzzy membership degrees to uncertainty handling. Experiments are conducted on the SemEval2007-Task14 dataset that contains 1250 annotated texts with Ekman’s basic emotions. The obtained results indicate that our proposed method outperforms the previous methods.
Assessment of Contributions of the Methodology for the Construction of a Catalog of Information Technology Services Aimed at Public Entities

Several field studies have shown low levels of implementation of this type of catalog in organizations in various countries around the world, which is accentuated in public organizations. To address this problem, the main objective of this research is to demonstrate the contribution of a methodology for the construction of a catalog of information technology services in the management of technology services aimed at public entities, which is tested and assessed in terms of its levels of contribution to the information management of technology services. The methodology used in this research was tested and evaluated by 46 IT professionals working in public organizations in Ecuador, verifying the contribution levels of the proposal in four aspects: 1) the identification of information technology services, 2) the classification of information technology services, 3) feedback from the service catalog as a transversal component, and 4) the automation of all the processes described. Favourable results were obtained regarding the proposed methodology.
Multimodal intent classification with incomplete modalities using text embedding propagation

Determining the author's intent in a social media post is a challenging multimodal task and requires identifying complex relationships between image and text in the post. For example, the post image can represent an object, person, product, or company, while the text can be an ironic message about the image content. Similarly, a text can be a news headline, while the image represents a provocation, meme, or satire about the news. Existing approaches propose intent classification techniques combining both modalities. However, some posts may have missing textual annotations. Hence, we investigate a graph-based approach that propagates available text embedding data from complete multimodal posts to incomplete ones. This paper presents a text embedding propagation method, which transfers embeddings from BERT neural language models to image-only posts (i.e., posts with incomplete modality) considering the topology of a graph constructed from both visual and textual modalities available during the training step. By using this inference approach, our method provides competitive results when textual modality is available at different completeness levels, even compared to reference methods that require complete modalities.
“How to get consensus with neighbors?”: Rating standardization for accurate collaborative filtering

In this paper, we present neighbor-aided rating standardization ( NARS ), a new framework for rating standardization that leverages the ratings of users’ neighbors for more accurate collaborative filtering. Our approach is motivated by the insight that users tend to give ratings to items according to different criteria of their own , which causes the accuracy degradation in item recommendation. Our NARS framework intelligently alleviates the difference in rating criteria among all users through rating standardization in the context of consensus with neighbors . Consensus, referred to as the process of reducing disagreement in rating criteria among users, is facilitated by effectively aggregating the ratings of all users. Consequently, the ratings adjusted with the unified rating criterion among all users ( i.e., standardized ratings) can be found via an iterative consensus process and are used as input of CF for top- N recommendation. Experimental results show that our proposed NARS framework consistently improves the accuracy of recommendation in terms of several accuracy metrics compared with various competing CF methods.
Restoration of degraded underwater images with attenuation coefficients as a clue

Abstract Underwater image restoration methods require the knowledge of wideband attenuation coefficients per color channel. In this paper, a Generative adversarial network(GAN) framework is proposed for underwater image restoration using the attenuation coefficients as an input. Attenuation coefficients like atmospheric light and transmission map are also estimated in order to restore an underwater image. For atmospheric light estimation, a combination of Res-net and Dense-net architecture is modelled and making it a learning based Residual-Dense Network. Through literature it is evident that, the transmission map describes about the portion of the light that is not scattered and reaches the camera. Transmission map is generated using the underwater image formation model. Further, these attenuation coefficients are given as an input to the proposed GAN architecture for underwater image restoration. The GAN architecture is having a generator block and a discriminator block. The result is demonstrated on synthetic and real underwater data-set and then compared with state-of-the-art method.