Reviewerly/exHarmony huggingface dataset:
Explainable B2B Recommender System for Potential Customer Prediction Using KGAT

The adoption of recommender systems in business-to-business (B2B) can make the management of companies more efficient. Although the importance of recommendation is increasing with the expansion of B2B e-commerce, not enough studies on B2B recommendations have been conducted. Due to several differences between B2B and business-to-consumer (B2C), the B2B recommender system should be defined differently. This paper presents a new perspective on the explainable B2B recommender system using the knowledge graph attention network for recommendation (KGAT). Unlike traditional recommendation systems that suggest products to consumers, this study focuses on recommending potential buyers to sellers. Additionally, the utilization of the KGAT attention mechanisms enables the provision of explanations for each company’s recommendations. The Korea Electronic Taxation System Association provides the Market Transaction Dataset in South Korea, and this research shows how the dataset is utilized in the knowledge graph (KG). The main tasks can be summarized in three points: (i) suggesting the application of an explainable recommender system in B2B for recommending potential customers, (ii) extracting the performance-enhancing features of a knowledge graph, and (iii) enhancing keyword extraction for trading items to improve recommendation performance. We can anticipate providing good insight into the development of the industry via the utilization of the B2B recommendation of potential customer prediction.
A coherence-witnessing game and applications to semi-device-independent quantum key distribution

Semi-device-independent quantum key distribution aims to achieve a balance between the highest level of security, device independence, and experimental feasibility. Semi-quantum key distribution presents an intriguing approach that seeks to minimize users&amp;apos; reliance on quantum operations while maintaining security, thus enabling the development of simplified and hardware fault-tolerant quantum protocols. In this work, we introduce a coherence-based, semi-device-independent, semi-quantum key distribution protocol built upon a noise-robust version of a coherence equality game that witnesses various types of coherence. Security is proven in the bounded quantum storage model, requiring users to implement only classical operations, specifically fixed-basis detections.
Deep recommendation system based on knowledge graph and review text

With the explosive increase of information, recommendation system is applied in a variety of areas. However, the performance of recommendation system is limited due to issues such as data sparsity, cold starts and poor semantic understanding. In order to make full use of external information to assist recommendation, deeply mine the semantic information of review text and further improve the performance of recommendation system, a deep recommendation system based on knowledge graph and review text (Drs-kgrt) is proposed in this paper. In Drs-kgrt, knowledge graph, review text and the social records between users are used as auxiliary information to improve recommendation performance. Firstly, the review text is divided into user review text and item review text. BERT (Bidirectional Encoder Representation from Transformers) is used to accurately understand semantic information in user review text and the social records between users. The trust relationship between users and user preferences are fully mined to form user feature vectors. Secondly, BERT and knowledge graph entity recognition link technology are combined to extract item attribute feature entities and their associated entities. The fine-grained features of the items are analyzed to form item feature vectors. Thirdly, based on the scoring matrix, latent vectors of users and items are obtained by matrix decomposition. The deep features of users and items are generated based on user feature vectors, item feature vectors, latent vectors of users and items, the deep recommendation system is established to predict user scores for items. Finally, experiments are conducted on the Douban dataset and Amazon Movie Review dataset, the results show that the proposed algorithm can achieve better performance compared with other benchmark recommendation algorithms.
Detecting offensive language using Chaotic Ant Lion optimization-based Ghost net in social media

Social media has become one of the most popular medium of communication and the post may be predominantly unstructured, informal, and frequently misspelled. It has become increasingly common for users to use abusive language in their comments. Detecting offensive language on social media platforms and the presence of such language on the Internet has become a major challenge for modern society. To overcome this challenge, Offensive Language Classification based on the Chaotic Antlion optimization algorithm has been proposed. Initially, the dataset is pre-processed using NLP languages for removing irrelevant data. Consequently, statistical, synthetic, and lexicon features are extracted using various feature extraction techniques. A Chaotic Antlion Optimization Algorithm is used to select the most relevant features during the feature selection phase. After selecting the features, a Ghost network classifies the input data into four classes namely offensive, non-offensive, swear, and offensive but not offensive. The proposed method was evaluated based on a number of variables, including precision, accuracy, specificity, recall, and F-measure. The best classification accuracy is achieved by the suggested method, which is 99.27% for the SOLID dataset and 98.99% for the OLID dataset. The suggested method outperforms the DCNN, Simple Logistics, and CNN methods in terms of overall accuracy by 4.99%, 8.72%, and 10.4%, respectively.
WORDY WORDY CARD FOR AN INNOVATIVE TEACHING METHOD FOR LEARNING ENGLISH

The foundation of learning a new language is vocabulary. Teaching vocabulary has been done in conjunction with teaching other language skills. Due to their little language knowledge, students continue to have trouble speaking. Meanwhile, senior high school students must get ready for either further education or the realm of industrial job. To address this issue, innovative teaching strategies in English are required. In this study, How Wordy card games can be used as innovative teaching tools to help kids in senior high school develop their vocabulary. Students in classes X, XI, and XII at SMK TI Bali Global Denpasar and SMK Negeri 2 Denpasar are the data sources. Techniques for gathering data that involve both direct observation and interviews. Data are analyzed using a descriptive qualitative approach, and they are then presented in a narrative format. The R&amp;D procedures were modified from Borg and Gall's (1983) ten-cycle method of creating wordy cards. This study created a special card game that can be played at all educational levels and is particularly beneficial to teachers in their vocabulary instruction.
Modified dipole-dipole interactions in the presence of a nanophotonic waveguide

When an emitter ensemble interacts with the electromagnetic field, dipole-dipole interactions are induced between the emitters. The magnitude and shape of these interactions are fully determined by the specific form of the electromagnetic field modes. If the emitters are placed in the vicinity of a nanophotonic waveguide, such as a cylindrical nanofiber, the complex functional form of these modes makes the analytical evaluation of the dipole-dipole interaction cumbersome and numerically costly. In this work, we provide a full detailed description of how to successfully calculate these interactions, outlining a method that can be easily extended to other environments and boundary conditions. Such exact evaluation is of importance as, due to the collective character of the interactions and dissipation in this kind of systems, any small modification of the interactions may lead to dramatic changes in experimental observables, particularly as the number of emitters increases. We illustrate this by calculating the transmission signal of the light guided by a cylindrical nanofiber in the presence of a nearby chain of emitters.
Three-Dimensional Flight Conflict Detection and Resolution Based on Particle Swarm Optimization

This paper presents a conflict detection and resolution method based on a velocity obstacle method for flight conflicts in a three-dimensional space. With the location and speed information of the two aircraft, the optimal relief strategy is obtained using particle swarm optimization. Aiming at the problem of excessive computational complexity in solving flight conflicts in a three-dimensional space with a cylindrical flight protection zone, an improved method for narrowing the search range is proposed to achieve a rapid solution by simplifying the complicated three-dimensional problem into a two-dimensional problem. The generality and flexibility of the method is effectively verified through simulations in flight conflict scenarios which almost cover all common situations. The experimental results show that the method can accurately determine the conflict time and generate the optimal relief strategy for different scenarios. The improved method of optimizing-search-range can significantly improve the computational efficiency, taking about 0.4 s to find the optimal solution, which can be used in real-time conflict resolution. The study provides a new solution for the flight conflict resolution problem.
BeneWinD: An Adaptive Benefit Win–Win Platform with Distributed Virtual Emotion Foundation

In recent decades, online platforms that use Web 3.0 have tremendously expanded their goods, services, and values to numerous applications thanks to its inherent advantages of convenience, service speed, connectivity, etc. Although online commerce and other relevant platforms have clear merits, offline-based commerce and payments are indispensable and should be activated continuously, because offline systems have intrinsic value for people. With the theme of benefiting all humankind, we propose a new adaptive benefit platform, called BeneWinD, which is endowed with strengths of online and offline platforms. Furthermore, a new currency for integrated benefits, the win–win digital currency, is used in the proposed platform. Essentially, the proposed platform with a distributed virtual emotion foundation aims to provide a wide scope of benefits to both parties, the seller and consumer, in online and offline settings. We primarily introduce features, applicable scenarios, and services of the proposed platform. Different from previous systems and perspectives, BeneWinD can be combined with Web 3.0 because it deliberates based on the decentralized or distributed virtual emotion foundation, and the virtual emotion feature and the detected virtual emotion information with anonymity are open to everyone who wants to participate in the platform. It follows that the BeneWinD platform can be connected to the linked virtual emotion data block or win–win digital currency. Furthermore, crucial research challenges and issues are addressed in order to make great contributions to improve the development of the platform.
A CNN Compression Method via Dynamic Channel Ranking Strategy

In recent years, the rapid development of mobile devices and embedded system raises a demand for intelligent models to address increasingly complicated problems. However, the complexity of the structure and extensive parameters press significantly on efficiency, storage space, and energy consumption. Additionally, the explosive growth of tasks with enormous model structures and parameters makes it impossible to compress models manually. Thus, a standardized and effective model compression solution achieving lightweight neural networks is established as an urgent demand by the industry. Accordingly, Dynamic Channel Ranking Strategy (DCRS) method is proposed to compress deep convolutional neural networks. DCRS selects channels with high contribution of each prunable layer according to compression ratio searched by reinforcement learning agent. Compared with current model compression methods, DCRS efficaciously applies various channel ranking strategies on prunable layers. Experiments indicate with a 50% compression ratio, compressed MobileNet achieved 70.62% top1 and 88.2% top5 accuracy on ImageNet, and compressed ResNet achieved 92.03% accuracy on CIFAR-10. DCRS reduces more FLOPS in these neural networks. The compressed model achieves the best Top-1 and Top-5 accuracy on ResNet50, the best Top-1 accuracy on MobilNetV1.
Leader learning loss function in neural network classification

Deep learning, based on Empirical Risk Minimization (ERM), typically aims to fit the ideal outputs of all samples due to its large capacity. However, models trained based on empirical losses like cross entropy (CE) or mean square error (MSE), often learn unnecessary information during classification, leading to premature overfitting. On the other hand, the result-focused loss functions, i.e., zero–one loss or hinge loss, are hard to optimize and thus are rarely applied directly in neural network. This paper proposes a novel leader learning in classification, where CE is gradually trained by classification results using sample-dependent cost-sensitive learning. As complementary, the stepwise-changed CE covers the deficiency on classification error while preserving the advantage of fast convergence. In this way, the deviation between CE and classification error can be corrected. Experimental results demonstrate that the proposed leader learning has a more significant convergence trend than the baseline algorithms. Moreover, the loss function learned from a specific dataset has broad generality that can be transferred to other models as prior knowledge.
An integer linear programming approach to solving the Eternity Puzzle

In this article, we develop a new integer linear programming (ILP) approach to solving the Eternity Puzzle, a hard combinatorial tiling problem using 209 polygonal puzzle pieces called polydrafters to exactly cover an almost-regular dodecagon. Previous approaches to solving the Eternity Puzzle used a refined brute-force approach, utilizing backtracking and statistical techniques that were tailored to the particular problem. Our method yields a flexible mathematical model of the tiling problem, expressed in the linear programming (LP) file format, which can be adapted for a wide range of similar puzzle problems of the Eternity type. The tiling problems are solved using a combination of programming in MATLAB, and the commercial high-performance optimization package CPLEX. We also describe the complex geometry of the Eternity Puzzle and use boundary words to represent the tiles and grids of any Eternity-type tiling puzzle. Our ILP approach is illustrated by solving four small puzzles of the Eternity-type, demonstrating that, with sufficient computing power, our approach would, in principle, yield a solution to the full Eternity Puzzle. Additionally, our publicly available set of MATLAB programs ETERNITY (v1.0.0) is available for download on Zenodo.org. These programs can be used to construct the ILP formulations, plot solutions, and be adapted by other researchers to investigate similar puzzle problems.
DeformSg2im: Scene graph based multi-instance image generation with a deformable geometric layout

Recently, scene graph based image generation has emerged to be an important research direction for advanced multi-instance generation tasks. Scene layout generation, the phase that produces the instance-wise visual representations and maintains the spatial relationships among all instances, is vital in translating a scene graph to an image. However, the scene layouts generated by existing methods are too coarse and semantically inconsistent with the given scene graph, which results in quality degradation of the generated images. Motivated by this, we propose a novel scene graph based image generation model called DeformSg2im, which aims to generate visually appealing and semantically faithful images according to the given scene graphs. Our method addresses the aforementioned problems from two aspects. In one aspect, we present an attention-based instance embedding estimator to refine the shape information for each instance. By introducing attention maps to the estimation of instance embeddings, our method is able to generate more plausible instances with sharp edges on the images. In the other aspect, a spatial warping network (SWN) is proposed to adaptively capture the spatial dependencies among instances. With sequential modeling and geometric deformations, the SWN is capable of generating the scene layout conforming to the given scene graph. Extensive experiments show that our model generates images with high visual quality and achieves competitive quantitative results compared to existing works. Ablation studies on the proposed modules are also conducted, and the results demonstrate the effectiveness of our method.
RATS: A regulatory anonymous transaction system based on blockchain

With the rapid development of digital currency such as Bitcoin, the digital currency transaction system with blockchain as the key underlying technology is booming, but the traditional transaction system has the risk of revealing sensitive information such as transaction content and identity. In order to enhance the security, many transactions system with privacy preserving have been proposed, such as Zerocoin, Monero, etc. As there are a decentralized credit entity and strong anonymity in these transaction systems, the authorities can effectively audit and control the participants and transactions in the digital currency system, thus making digital currency a tool for illegal transactions. Recognizing the importance of privacy preserving and regulatory, in this paper, we propose a new regulatory anonymous transaction system based on blockchain—RATS, which can not only protect the privacy of transactions on the blockchain, but also regulate illegal transactions. Firstly, we introduce regulators into the system, and at the same time, we propose a regulatory mechanism that the regulator is allowed to trace the identity of the user without affecting the transaction operation of the system when suspicious transactions are found. In particularly, during the normal transaction, we protect the privacy of users' transaction contents and addresses without affecting the anonymity of the original system. We formalize the system model and security model of RATS. Moreover, the security of the proposed scheme is strictly proved and analyzed.
Slow feature subspace: A video representation based on slow feature analysis for action recognition

This paper proposes a new video representation for subspace-based action recognition. Traditional subspace-based methods represent a video as a subspace by applying principal component analysis (PCA) to its frames. However, this subspace might lead to an imprecise representation of actions, as PCA loses the temporal information of frames. Therefore, we introduce the slow feature subspace based on the slow feature analysis (SFA). SFA extracts a set of slow features of an input video by projecting the video frames onto the weight vectors that minimize the data variance over time. Motivated by these properties, several methods based on SFA were proposed for action recognition. However, they do not explicitly consider the distribution of the slow features, which represents essential action information. Therefore, our key idea is to capture this distribution through a low-dimensional subspace called slow feature subspace. Our subspace is generated by applying PCA to several weight vectors corresponding to the slowest components obtained by SFA. Our framework replaces the subspaces of several traditional mutual subspace methods with our slow feature subspace to improve their results in action recognition. This approach transforms the comparison between two videos into the comparison between two slow feature subspaces using the canonical angles between them, avoiding vector concatenation and data aggregation. The effectiveness of our framework is demonstrated through extensive experiments with various datasets. Our results show that our slow feature subspace can improve the traditional subspace-based methods and achieve competitive performance compared to different methods, including state-of-the-art neural networks.
Joint learning of images and videos with a single Vision Transformer

In this study, we propose a method for jointly learning of images and videos using a single model. In general, images and videos are often trained by separate models. We propose in this paper a method that takes a batch of images as input to Vision Transformer (IV-ViT), and also a set of video frames with temporal aggregation by late fusion. Experimental results on two image datasets and two action recognition datasets are presented.
PROFINET Security: A Look on Selected Concepts for Secure Communication in the Automation Domain

We provide a brief overview of the cryptographic security extensions for PROFINET, as defined and specified by PROFIBUS & PROFINET International (PI). These come in three hierarchically defined Security Classes, called Security Class 1,2 and 3. Security Class 1 provides basic security improvements with moderate implementation impact on PROFINET components. Security Classes 2 and 3, in contrast, introduce an integrated cryptographic protection of PROFINET communication. We first highlight and discuss the security features that the PROFINET specification offers for future PROFINET products. Then, as our main focus, we take a closer look at some of the technical challenges that were faced during the conceptualization and design of Security Class 2 and 3 features. In particular, we elaborate on how secure application relations between PROFINET components are established and how a disruption-free availability of a secure communication channel is guaranteed despite the need to refresh cryptographic keys regularly. The authors are members of the PI Working Group CB/PG10 Security.
New Approach for Effective Twitter Sentiments Analysis

People can share and express their perspectives on various topics, have discussions with various communities, or post global messages on social networking sites such as Twitter, Facebook, and Google. These sites are quickly gaining popularity because they enable people to do these things. The discipline of analyzing people’s feelings based on the content of tweets on Twitter has seen much progress recently. Finding the most effective sentiment classifier to place tweets in the appropriate categorization is the most challenging aspect of conducting Twitter sentiment analysis. As a result of this study’s findings, a new and improved approach to word representation has been suggested. This approach uses the contribution of sentiment information into the conventional TF-IDF algorithm and generates weighted word vectors. This research provides a comparison between the two methods (TF-IDF and VADER), in addition to the use of machine learning and deep learning algorithms (KNN, RF, LSTM, and ANN), to choose the best algorithms to build a robust model that is capable of accurately analyzing sentiments. This comparison aims to determine which algorithms are the most effective for building such a model. The findings demonstrate that using TF-IDF in conjunction with the RF algorithm results in an accuracy of 98.7%, more significant than the accuracy achieved by other algorithms employing the same TF-IDF or the other VADER technique.