Reviewerly/exHarmony huggingface dataset:
Explainable Cross-Topic Stance Detection for Search Results

One way to help users navigate debated topics online is to apply stance detection in web search. Automatically identifying whether search results are against, neutral, or in favor could facilitate diversification efforts and support interventions that aim to mitigate cognitive biases. To be truly useful in this context, however, stance detection models not only need to make accurate (cross-topic) predictions but also be sufficiently explainable to users when applied to search results – an issue that is currently unclear. This paper presents a study into the feasibility of using current stance detection approaches to assist users in their web search on debated topics. We train and evaluate 10 stance detection models using a stance-annotated data set of 1204 search results. In a preregistered user study (N = 291), we then investigate the quality of stance detection explanations created using different explainability methods and explanation visualization techniques. The models we implement predict stances of search results across topics with satisfying quality (i.e., similar to the state-of-the-art for other data types). However, our results reveal stark differences in explanation quality (i.e., as measured by users’ ability to simulate model predictions and their attitudes towards the explanations) between different models and explainability methods. A qualitative analysis of textual user feedback further reveals potential application areas, user concerns, and improvement suggestions for such explanations. Our findings have important implications for the development of user-centered solutions surrounding web search on debated topics.
Actual rating calculation of the zoom cloud meetings app using user reviews on google play store with sentiment annotation of BERT and hybridization of RNN and LSTM

The recent outbreaks of the COVID-19 forced people to work from home. All the educational institutes run their academic activities online. The online meeting app the "Zoom Cloud Meeting" provides the most entire supports for this purpose. For providing proper functionalities require in this situation of online supports the developers need the frequent release of new versions of the application. Which makes the chances to have lots of bugs during the release of new versions. To fix those bugs introduce developer needs users' feedback based on the new release of the application. But most of the time the ratings and reviews are created contraposition between them because of the users' inadvertent in giving ratings and reviews. And it has been the main problem to fix those bugs using user ratings for software developers. For this reason, we conduct this average rating calculation process based on the sentiment of user reviews to help software developers. We use BERT-based sentiment annotation to create unbiased datasets and hybridize RNN with LSTM to find calculated ratings based on the unbiased reviews dataset. Out of four models trained on four different datasets, we found promising performance in two datasets containing a necessarily large amount of unbiased reviews. The results show that the reviews have more positive sentiments than the actual ratings. Our results found an average of 3.60 stars rating, where the actual average rating found in dataset is 3.08 stars. We use reviews of more than 250 apps from the Google Play app store. The results of our can provide more promising if we can use a large dataset only containing the reviews of the Zoom Cloud Meeting app.
MoCo4SRec: A momentum contrastive learning framework for sequential recommendation

Sequential recommendation (SR) is an essential component of modern recommender systems. It models the dynamic interest of users based on their sequential interactions. Recently, several studies have utilized sequential deep learning models such as Recurrent Neural Networks and Transformers to facilitate sequential recommendation, which shows promising results. Inspired by the rise of contrastive learning techniques, some methods are devoted to enhance sequential deep learning models by designing contrastive learning loss on self-supervised signals. However, there are still a number of obstacles that make it challenging to efficiently learn user representations by contrastive learning. These issues include, but are not limited to, data sparsity, noisy data, and sampling bias (e.g., false negative), particularly in complex, parameter-intensive models. In light of these challenges, we examine how to deal with data sparsity and noisy data by implementing contrastive Self-Supervised Learning (SSL) and Momentum Contrast (MoCo) to the sequential recommendation. Except typical in-batch negatives, our basic idea is to maintain a dynamic queue to expand negative samples with a moving-averaged encoder. After being augmented by sequence-level and embedding-level methods, the representations from all historical encoder output are pushed into the dynamic queue, which usually leads to sampling bias when potential positives in the queue are used as expanded negative samples in contrastive learning. To tackle this issue, we integrate momentum updating mechanism with a novel instance weighting mechanism to penalize false negatives and guarantee the model’s efficacy. To this end, we introduce a fresh framework called Momentum Contrastive Learning Framework for Sequential Recommendation (MoCo4SRec). Experiments on eight real-world datasets demonstrate how the suggested strategy might boost model performance over current benchmarks by adopting improved user representations.
A blind watermarking algorithm based on specific component quantization in DHT domain

For meeting the higher demands of color image ownership protection in the fifth-generation (5 G) communication network, a blind watermarking algorithm based on specific component quantization of discrete Hartley transform (DHT) is proposed. At first, the color cover image is partitioned into the non-overlapping blocks of sized 4 × 4, and the DHT component of the selected block at specific location is calculated; then the inherent correlation in RGB channels and quantization index modulation (QIM) technique are used to obtain the component difference; finally, without global DHT of forward and inverse, the color watermark logo is inserted into the cover image by the derived modification method. Moreover, RSA public-key encryption algorithm is applied to scramble the parameters of Affine transform, which guarantees the double-layer security of watermark itself and algorithm secret keys. When extracting watermark, an image correction strategy based on geometric features is realized to resist geometric distortion. Experiment results show that the proposed algorithm has low computational complexity, strong robustness and high security at the foundation of good invisibility and large payload, which overcomes the shortcoming of slow execution speed of transform-domain watermarking and has high feasibility of fast copyright protection in 5 G environment.
The integration of Blockchain and AI for Web 3.0: A security Perspective

A decentralized and secure architecture made possible by blockchain technology is what Web 3.0 is known for. By offering a secure and trustworthy platform for transactions and data storage, this new paradigm shift in the digital world promises to transform the way we interact with the internet. Data is the new oil, thus protecting it is equally crucial. The foundation of the web 3.0 ecosystem, which provides a secure and open method of managing user data, is blockchain technology. With the launch of Web 3.0, demand for seamless communication across numerous platforms and technologies has increased. Blockchain offers a common framework that makes it possible for various systems to communicate with one another. The decentralized nature of blockchain technology almost precludes hacker access to the system, ushering in a highly secure Web 3.0. By preserving the integrity and validity of data and transactions, blockchain helps to build trust in online transactions. AI can be integrated with blockchain to enhance its capabilities and improve the overall user experience. We can build a safe and intelligent web that empowers users, gives them more privacy, and gives them more control over their online data by merging blockchain and AI. In this article, we emphasize the value of blockchain and AI technologies in achieving Web 3.0's full potential for a secure internet and propose a Blockchain and AI empowered framework. The future of technology is now driven by the power of blockchain, AI, and web 3.0, providing a secure and efficient way to manage digital assets and data.
FRoMEPP: Digital forensic readiness framework for material extrusion based 3D printing process

Additive manufacturing (a.k.a., 3D printing) materializes an object by stacking thin layers of material from ground zero. It is increasingly utilized in industry to print critical components of automobiles, airplanes, etc. Failure of a 3D printed part (such as a turbine blade) during operation may incur immense damage to the system and the surroundings, incentivizing cyberattacks on the printed object. A forensically ready printing setup facilitates a post incident investigation. Currently, no forensic readiness model exists for an additive manufacturing (AM) process in the literature, whereas conventional cyber-domain specific models do not consider AM processes and may be ineffective in investigating 3D printed parts in a crime scene. This paper presents a forensic readiness framework, FRoMEPP for the material extrusion-based 3D printing process to acquire and preserve forensic data after identifying important information sources in the printing process chain. FRoMEPP framework provides practical technical guidance to the organizations striving for a forensically ready printing environment. It also benefits the regulatory bodies in formalizing compliance criteria for critical 3D printing setups. We implement FRoMEPP framework on a typical material-extrusion printer, Ultimaker-3, and evaluate it through a case study by implementing three sabotage attacks involving thermal profile manipulation, internal voids, and printing timing integrity compromise. The evaluation results show that FRoMEPP can effectively investigate and present traces of the attacks against 3D printed parts.
Demonstration of the charging progress of quantum batteries

The authors employ single photons and linear optics to simulate a two-qubit quantum battery and investigate the relationship between the performance of quantum batteries and the amount of entanglement and coherence that arise during the charging process. The results show that entanglement is not always the most important resource to boost charging, while coherence plays a nontrivial role.
Efficient Resource Management of Micro-Services in VANETs

While vehicular ad hoc Networks (VANETs) are relatively well-studied, a number of challenges remain, particularly as autonomous vehicles become more commonplace. For example, devices on a vehicle such as, On-Board Units (OBUs) may have resource constraints which render them incapable of supporting computationally expensive cryptography operations required to achieve various security features. One potential solution is to offload computationally expensive tasks to other nodes in the VANET; however, the dynamic nature of the setup (such as the, mobility of the requesting vehicles and other nodes) compounds the challenge of resource management. In this context, we propose a scheme that uses Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to achieve data confidentiality in VANETs despite such challenges. Specifically, we build a cluster of vehicles to perform CP-ABE operations without relying on other nodes in the VANET. We use Kubernetes, an open-source container orchestration system, to build vehicle cluster(s) to handle distributed micro-tasks. In this scheme, we use a set of factors that impact the computation operations in cluster vehicle components (i.e., the OBU). Each factor, including the distance between the data owner vehicle and the target vehicle, the duration of each target vehicle in the cluster, and the resource of each vehicle in the cluster, has a weight based on its influence in computational operations. The Euclidean method is used to calculate the weight value for each factor. Based on the final total weight for each vehicle, our approach distributes the tasks between vehicles. We evaluate our results by comparing our approach with the mechanism of Kubernetes for task distribution, which only considers the resources in each vehicle. We also consider several scenarios with varying factors to evaluate their impact on the execution time of CP-ABE on OBUs in addition to using simulations to evaluate the performance of our approach in terms of transmission and propagation overheads for vehicles in the cluster.
An Effective WBC Segmentation and Classification Using MobilenetV3–ShufflenetV2 Based Deep Learning Framework

White Blood Cells are essential in keeping track of a person’s health. However, the pathologist’s experience will determine how the blood smear is evaluated. Furthermore, it is still challenging to classify WBCs accurately because they have various forms, sizes, and colors due to distinct cell subtypes and labeling methods. As a result, a powerful deep learning system for WBC categorization based on MobilenetV3-ShufflenetV2 is described in this research. Initially, the WBC images are segmented using an efficient Pyramid Scene Parsing Network (PSPNet). Following that, MobilenetV3 and an Artificial Gravitational Cuckoo Search (AGCS)-based technique are used to extract and select the global and local features from the segmented images. Finally, the WBC images are divided into five classes using a ShufflenetV2 model. The proposed approach is evaluated on blood cell count and detection (BCCD) and Raabin-Wbc datasets and achieves 99.19% and 99% accuracy, respectively. Moreover, the results are satisfactory when compared to existing algorithms.
Multi-order Replay Attack Detection Using Enhanced Feature Extraction and Deep Learning Classification

The authenticated users are identified and verified using automatic speaker verification (ASV) technologies. An automatic speaker verification (ASV) system, like any other user identification system, is also sensitive to spoofing. In order to make the ASV systems robust against spoofing, these systems are alienated into two different phases, i.e., frontend feature extraction and backend classification model. The main emphasis of the paper is on the development of the system against multi-order replay attacks. The joint frequency-domain linear prediction (FDLP) and mel-frequency cepstral coefficients (MFCC) is used at frontend to extract the features from the audio samples. At backend, gated recurrent unit (GRU) classification model is used. The proposed system is achieving 2.99% equal error rate (ERR) and 1.6% ERR under 1PR and 2PR spoofing attacks, respectively, and also provides 97.7% and 97.9% accuracy under the same environment.
TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning

Abstract Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an intriguing question – if the combination of image restoration and object detection, can boost the performance of cutting‐edge detectors in adverse weather conditions. To answer it, we propose an effective yet unified detection paradigm that bridges these two subtasks together via dynamic enhancement learning to discern objects in adverse weather conditions, called TogetherNet. Different from existing efforts that intuitively apply image dehazing/deraining as a pre‐processing step, TogetherNet considers a multi‐task joint learning problem. Following the joint learning scheme, clean features produced by the restoration network can be shared to learn better object detection in the detection network, thus helping TogetherNet enhance the detection capacity in adverse weather conditions. Besides the joint learning architecture, we design a new Dynamic Transformer Feature Enhancement module to improve the feature extraction and representation capabilities of TogetherNet. Extensive experiments on both synthetic and real‐world datasets demonstrate that our TogetherNet outperforms the state‐of‐the‐art detection approaches by a large margin both quantitatively and qualitatively. Source code is available at https://github.com/yz-wang/TogetherNet .
MODNet: Multi‐offset Point Cloud Denoising Network Customized for Multi‐scale Patches

The intricacy of 3D surfaces often results cutting-edge point cloud denoising (PCD) models in surface degradation including remnant noise, wrongly-removed geometric details. Although using multi-scale patches to encode the geometry of a point has become the common wisdom in PCD, we find that simple aggregation of extracted multi-scale features can not adaptively utilize the appropriate scale information according to the geometric information around noisy points. It leads to surface degradation, especially for points close to edges and points on complex curved surfaces. We raise an intriguing question -- if employing multi-scale geometric perception information to guide the network to utilize multi-scale information, can eliminate the severe surface degradation problem? To answer it, we propose a Multi-offset Denoising Network (MODNet) customized for multi-scale patches. First, we extract the low-level feature of three scales patches by patch feature encoders. Second, a multi-scale perception module is designed to embed multi-scale geometric information for each scale feature and regress multi-scale weights to guide a multi-offset denoising displacement. Third, a multi-offset decoder regresses three scale offsets, which are guided by the multi-scale weights to predict the final displacement by weighting them adaptively. Experiments demonstrate that our method achieves new state-of-the-art performance on both synthetic and real-scanned datasets.
User‐Controllable Latent Transformer for StyleGAN Image Layout Editing

Abstract Latent space exploration is a technique that discovers interpretable latent directions and manipulates latent codes to edit various attributes in images generated by generative adversarial networks (GANs). However, in previous work, spatial control is limited to simple transformations (e.g., translation and rotation), and it is laborious to identify appropriate latent directions and adjust their parameters. In this paper, we tackle the problem of editing the StyleGAN image layout by annotating the image directly. To do so, we propose an interactive framework for manipulating latent codes in accordance with the user inputs. In our framework, the user annotates a StyleGAN image with locations they want to move or not and specifies a movement direction by mouse dragging. From these user inputs and initial latent codes, our latent transformer based on a transformer encoder‐decoder architecture estimates the output latent codes, which are fed to the StyleGAN generator to obtain a result image. To train our latent transformer, we utilize synthetic data and pseudo‐user inputs generated by off‐the‐shelf StyleGAN and optical flow models, without manual supervision. Quantitative and qualitative evaluations demonstrate the effectiveness of our method over existing methods.
Competency Functional Profile of the University Teacher: Value Approach

The article presents the result of developing the model of the competence profile of a teacher as the basis for the system of revealing, assessing and developing professional skills of a teacher, including competences, which have a significant impact on the quality of pedagogical and scientific work of a teacher. The emphasis is put on different levels of competence profile: functional (competencies necessary for teachers to develop and disseminate research, innovation, and educational practices) and value (competencies acquired and developed by a teacher under the influence of stimulating professional environment and values of corporate culture of professional community and HEI). The empirical basis of the article is the results of a series of focus groups and strategy sessions with teaching and administrative staff and students reflecting the peculiarities of perception of “professional mastery of a teacher” perception as a defining category of quality pedagogical process in higher education.
Outlier detection in social networks leveraging community structure

Social networks have become an important aspect of our modern times and are gradually becoming an integral means of communication worldwide. Overwhelming amounts of data are being transferred over social networks every day. Hence ensuring security becomes a necessity. Suspicious users or spammers may pose a threat to the information and data shared by users over the network. With this in mind, outliers detection is a crucial aspect of network communication. In our paper, a new technique is proposed to identify the anomalies in a network from a global perspective by using the network community structure. In general, state-of-the-art outliers detection algorithms mainly focus on the individual nodes and their direct neighborhood. But our technique considers only those nodes which tend to belong to multiple communities or whose neighbors belong to the same community or do not belong to any community. Results after experiments on synthetic and real-world networks show an improvement of 7-10% and 29% in F-Score and Jaccard similarity, respectively, compared to the state-of-the-art algorithms. Furthermore, we achieve almost 1.83 times speedup compared to the state-of-the-art algorithms.
Store, share and transfer: Learning and updating sentiment knowledge for aspect-based sentiment analysis

Previous studies have shown that incorporating sentiment knowledge (e.g., sentiment scores) is effective for aspect-based sentiment analysis (ABSA). However, sentiment knowledge is used to create static features, which cannot be propagated over an entire corpus. Unlike previous researchers, we designed a corpus-level sentiment knowledge fusion mechanism with storage, update, and sharing functions, which can help the model to better understand the sentiment information of various opinion words in the dataset. Specifically, we first constructed a dependency graph for each sentence and refined the weights of the edges by the relative distance between the aspect terms and context words. We then introduced two special sentiment knowledge nodes in the graph to establish connections with opinion words by leveraging external sentiment lexicons. We set these two nodes to be globally shared and updatable, which allowed the model to learn corpus-level and domain-specific sentiment knowledge. This knowledge can help the model to generate a better aspect representation that contains rich contextual information and sentiment knowledge. Extensive experiments were conducted on several public datasets, and the experimental results demonstrated the effectiveness of our method. We also analyzed the performance gains from using learned corpus-level sentiment knowledge to transfer across different datasets.