Reviewerly/exHarmony huggingface dataset:
Communicative Strategies and Tactics of Trolls-Aggressors in Computer-Mediated Communication during COVID-19 Pandemic

The article considers the aggressive communicative behaviour of a virtual linguistic personality provoking a communication conflict in the virtual space (a troll-aggressor). The data include comments retrieved from LiveJournal posts and Internet memes published during the COVID-19 pandemic. In the course of the analysis, the authors reveal the communicative roles, strategies and tactics of trolls-aggressors. The common strategy of the troll’s aggressive conflict interaction in the indicated time period is the strategy of hostility which is embodied in particular strategies (micro-strategies) of discrediting and attack. It is concluded that discrediting as a micro-strategy can be implemented via a false compliment, the tactic of taunt, the tactic of insult and the tactic of blaming. The micro-strategy of attack is implemented by means of threat and indignation. Particular attention is paid to the description of the markers of the above-listed tactics observed in trolls-aggressors’ communicative behaviour. Enumeration of the tactics used for provoking conflicts in virtual environment made it possible to concretize the typology of trolls-aggressors which has been revealed in the course of empiric data analysis. As a result, the authors present a scheme which reflects the types of trolls, belonging to the general type “troll-aggressor”, and the implemented communicative strategies and tactics within the framework of the common strategy of hostility.
Supply chain management

A good blockchain use case is one that yields measurable results of values for companies engaged in economic transactions that cannot be possible with other alternatives. This chapter argues that supply chain is one such use case that can really benefit from blockchain. The Netherlands-based market intelligence platform Blockdata's analysis found that traceability and provenance in supply chains was the most popular blockchain use case among the world's biggest brands in 2020. Supply chains have many features that cannot be solved with huge database alone. Complex global Supply chain have no central authority. Companies involved in a supply chain can benefit from transparency, commercial confidentiality of data and an immutable record of transactions. This chapter gives an overview of how blockchain can facilitate the management of the flow of goods and services as well as key processes involved in the transformation raw materials into final products. It promotes an understanding of how blockchain is likely to affect key supply chain management objectives such as cost, quality, speed, dependability, risk reduction, sustainability and flexibility. It also provides a detailed analysis and description of blockchain's roles in tracking the sources of insecurity in supply chains related to Internet of Things devices. A key focus of this chapter is also on the role of blockchain to enhance supply chain visibility.
Radio signal modulation recognition algorithm based on convolutional neural network

In modern communications, it is often necessary to correctly identify the modulation of a signal with almost zero a priori knowledge as a non-collaborator for subsequent work such as demodulation and analysis. This paper is based on the convolutional neural network algorithm to study the modulation recognition algorithm of radio signals. Firstly, this paper studies the format, parameters and channel model of the radio signal dataset, and constructs a dataset in the format of I/Q data. Then, this article uses Convolutional Neural Network (CNN) algorithm to identify and classify the signal. Finally, to address the defects and training time problems in the classical convolutional neural network recognition method, this paper proposes an improved convolutional neural network named X-CNN. The X-CNN is optimised in terms of connection structure, enhancement mechanism and pooling method to improve the recognition rate and the overall fitting ability of the network.
Transparent, simple and robust fast-and-frugal trees and their construction

Today, diagnostic reasoning combines common and specialized knowledge, elements of numeracy, some facility with the basics of probability theory and, last but not least, ease in interactions with AI tools. We present procedures and tools for constructing trees that lead to understandable, transparent, simple, and robust classifications and decisions. These tools are more heuristic than optimal models, inspired by the perspective of Bounded Rationality. We describe how the tenets of Bounded Rationality provide a framework for the human-machine interaction this paper is devoted to. We claim that, because of this rationality, our proposed tools facilitate machine-aided decision making that is smooth, transparent and successful.
Synthesizing Counterfactual Samples for Effective Image-Text Matching

Image-text matching is a fundamental research topic bridging vision and language. Recent works use hard negative mining to capture the multiple correspondences between visual and textual domains. Unfortunately, the truly informative negative samples are quite sparse in the training data, which are hard to obtain only in a randomly sampled mini-batch. Motivated by causal inference, we aim to overcome this shortcoming by carefully analyzing the analogy between hard negative mining and causal effects optimizing. Further, we propose Counterfactual Matching (CFM) framework for more effective image-text correspondence mining. CFM contains three major components, \ie, Gradient-Guided Feature Selection for automatic casual factor identification, Self-Exploration for causal factor completeness, and Self-Adjustment for counterfactual sample synthesis. Compared with traditional hard negative mining, our method largely alleviates the over-fitting phenomenon and effectively captures the fine-grained correlations between image and text modality. We evaluate our CFM in combination with three state-of-the-art image-text matching architectures. Quantitative and qualitative experiments conducted on two publicly available datasets demonstrate its strong generality and effectiveness. Code is available at: https://github.com/weihao20/cfm.
Detach and Attach: Stylized Image Captioning without Paired Stylized Dataset

Stylized Image Captioning aims to generate captions with accurate image content and stylized elements simultaneously. However, large-scaled image and stylized caption pairs cost lots of resources and are usually unavailable. Therefore, it's a challenge to generate stylized captions without paired stylized caption dataset. Previous work on controlling the style of generated captions in an unsupervised way can be divided into two ways: implicitly and explicitly. The former mainly relies on a well-trained language model to capture style knowledge, which is limited to a single style and hard to handle multi-style task. Thus, the latter uses extra style constraints such as outlined style labels or stylized words extracted from stylized sentences to control the style rather than the trained style-specific language model. However, certain styles, such as humorous and romance, are implied in the whole sentence, instead of in some words of a sentence. To address the problems above, we propose a two-step method based on Transformer: firstly detach style representations from large-scaled stylized text-only corpus to provide more holistic style supervision, and secondly attach the style representations to image content to generate stylized captions. We learn a shared image-text space to narrow the gap between the image and the text modality for better attachment. Due to the trade-off between semantics and style, we explore three injection methods of style representations to balance two requirements of image content preservation and stylization. Experiments show that our method outperforms the state-of-the-art systems in overall performance, especially on implied styles.
A Conversational Shopping Assistant for Online Virtual Stores

The online shopping industry benefits from the usage of virtual assistants that are able to provide a 24/7 way of communication. This is normally done through the use of text based interactions. In order to improve the user experience and provide a more engaging user-to-customer interaction, we present a virtual shopping environment that emulates the physical store, providing the user with an experience similar to buying products in the actual store. This allows users to interact with the store items and have a full conversation with a virtual shopping assistant. This is all done through a virtual store where the user is able to walk, try-on and see the displayed items, while having a conversation with the store's assistant.
LS-GAN: Iterative Language-based Image Manipulation via Long and Short Term Consistency Reasoning

Iterative language-based image manipulation aims to edit images step by step according to user's linguistic instructions. The existing methods mostly focus on aligning the attributes and appearance of new-added visual elements with current instruction. However, they fail to maintain consistency between instructions and images as iterative rounds increase. To address this issue, we propose a novel Long and Short term consistency reasoning Generative Adversarial Network (LS-GAN), which enhances the awareness of previous objects with current instruction and better maintains the consistency with the user's intent under the continuous iterations. Specifically, we first design a Context-aware Phrase Encoder (CPE) to learn the user's intention by extracting different phrase-level information about the instruction. Further, we introduce a Long and Short term Consistency Reasoning (LSCR) mechanism. The long-term reasoning improves the model on semantic understanding and positional reasoning, while short-term reasoning ensures the ability to construct visual scenes based on linguistic instructions. Extensive results show that LS-GAN improves the generation quality in terms of both object identity and position, and achieves the state-of-the-art performance on two public datasets.
Adaptive Transformer-Based Conditioned Variational Autoencoder for Incomplete Social Event Classification

With the rapid development of the Internet and the expanding scale of social media, incomplete social event classification has increasingly become a challenging task. The key for incomplete social event classification is to accurately leverage the image-level and text-level information. However, most of the existing approaches may suffer from the following limitations: (1) Most Generative Models use the available features to generate the incomplete modality features for social events classification while ignoring the rich semantic label information. (2) The majority of existing multi-modal methods just simply concatenate the coarse-grained image features and text features of the event to get the multi-modal features to classify social events, which ignores the irrelevant multi-modal features and limits their modeling capabilities. To tackle these challenges, in this paper, we propose an Adaptive Transformer-Based Conditioned Variational Autoencoder Network (AT-CVAE) for incomplete social event classification. In the AT-CVAE, we propose a novel Transformer-based Conditioned Variational Autoencoder to jointly model the textual information, visual information and label information into a unified deep model, which can generate more discriminative latent features and enhance the performance of incomplete social event classification. Furthermore, the Mixture-of-Experts Mechanism is utilized to dynamically acquire the weights of each multi-modal information, which can better filter out the irrelevant multi-modal information and capture the vitally important information. Extensive experiments are conducted on two public event datasets, demonstrating the superior performance of our AT-CVAE method.
Bidirectionally Learning Dense Spatio-temporal Feature Propagation Network for Unsupervised Video Object Segmentation

Spatio-temporal feature representation is essential for accurate unsupervised video object segmentation, which needs an effective feature propagation paradigm for both appearance and motion features that can fully interchange information across frames. However, existing solutions mainly focus on the forward feature propagation from the preceding frame to the current one, either using the former segmentation mask or motion propagation in a frame-by-frame manner. This ignores the bi-directional temporal feature interactions (including the backward propagation from the future to the current frame) across all frames that can help to enhance the spatiotemporal feature representation for segmentation prediction. To this end, this paper presents a novel Dense Bidirectional Spatio-temporal feature propagation Network (DBSNet) to fully integrate the forward and the backward propagations across all frames. Specifically, a dense bi-ConvLSTM module is first developed to propagate the features across all frames in a forward and backward manner. This can fully capture the multi-level spatio-temporal contextual information across all frames, producing an effective feature representation that has a strong discriminative capability to tell from noisy backgrounds. Following it, a spatio-temporal Transformer refinement module is designed to further enhance the propagated features, which can effectively capture the spatio-temporal long-range dependencies among all frames. Afterwards, a Co-operative Direction-aware Graph Attention (Co-DGA) module is designed to integrate the propagated appearancemotion cues, yielding a strong spatio-temporal feature representation for segmentation mask prediction. The Co-DGA assigns proper attentional weights to neighboring points along the coordinate axis, making the segmentation model to selectively focus on the most relevant neighbors. Extensive evaluations on four mainstream challenging benchmarks including DAVIS16, FBMS, DAVSOD, and MCL demonstrate that the proposed DBSNet achieves favorable performance against state-of-the-art methods in terms of all evaluation metrics.
Towards a hybrid user interface for the visual exploration of large biomolecular networks using virtual reality

Abstract Biomolecular networks, including genome-scale metabolic models (GSMMs), assemble the knowledge regarding the biological processes that happen inside specific organisms in a way that allows for analysis, simulation, and exploration. With the increasing availability of genome annotations and the development of powerful reconstruction tools, biomolecular networks continue to grow ever larger. While visual exploration can facilitate the understanding of such networks, the network sizes represent a major challenge for current visualisation systems. Building on promising results from the area of immersive analytics, which among others deals with the potential of immersive visualisation for data analysis, we present a concept for a hybrid user interface that combines a classical desktop environment with a virtual reality environment for the visual exploration of large biomolecular networks and corresponding data. We present system requirements and design considerations, describe a resulting concept, an envisioned technical realisation, and a systems biology usage scenario. Finally, we discuss remaining challenges.
Arbitrary Bit-width Network: A Joint Layer-Wise Quantization and Adaptive Inference Approach

Conventional model quantization methods use a fixed quantization scheme to different data samples, which ignores the inherent"recognition difficulty" differences between various samples. We propose to feed different data samples with varying quantization schemes to achieve a data-dependent dynamic inference, at a fine-grained layer level. However, enabling this adaptive inference with changeable layer-wise quantization schemes is challenging because the combination of bit-widths and layers is growing exponentially, making it extremely difficult to train a single model in such a vast searching space and use it in practice. To solve this problem, we present the Arbitrary Bit-width Network (ABN), where the bit-widths of a single deep network can change at runtime for different data samples, with a layer-wise granularity. Specifically, first we build a weight-shared layer-wise quantizable "super-network" in which each layer can be allocated with multiple bit-widths and thus quantized differently on demand. The super-network provides a considerably large number of combinations of bit-widths and layers, each of which can be used during inference without retraining or storing myriad models. Second, based on the well-trained super-network, each layer's runtime bit-width selection decision is modeled as a Markov Decision Process (MDP) and solved by an adaptive inference strategy accordingly. Experiments show that the super-network can be built without accuracy degradation, and the bit-widths allocation of each layer can be adjusted to deal with various inputs on the fly. On ImageNet classification, we achieve 1.1% top1 accuracy improvement while saving 36.2% BitOps.
Interior Distance Ratio to a Regular Shape for Fast Shape Recognition

A fast shape recognition method based on regular graphic is proposed in this paper. It is the Interior Distance Ratio to a regular Shape (minimum bounding rectangle (MBR) or minimum circumscribed circle (MCC)) (SIDR). Regular shapes themselves have either axisymmetric or origin symmetry, which gives them regularity. Shape, as a feature of an object, plays a significant role in computer vision and image analysis. The shape descriptor is widely used to compute remarkable features of the visual image, especially in image understanding and analysis. SIDR is a new remarkable feature of the shape, which is the distribution of the interior distance between the shape contour points and its minimum bounding rectangle or minimum circumscribed circle. It can provide more effective performance support for practical application fields of computer vision, such as object detection and recognition. The minimum bounding rectangle or minimum circumscribed circle can change according to the change in a shape’s position, scale and direction, which is extremely suitable for describing a shape that has deformation. In addition, the rectangularity and circularity derived from them also have the potential peculiarity to describe the shape feature. Therefore, this paper uses the interior distance ratio of the shape to represent the shape feature. First, the minimum bounding rectangle or minimum circumscribed circle of the shape is selected according to the rectangularity and circularity of the shape. Then, the interior distance proportional distribution from the shape contour point to the minimum bounding rectangle or minimum circumscribed circle is obtained. Finally, a histogram is used to represent the distribution feature, and shape matching and recognition are carried out. A self-built dataset and three international generic datasets are used to verify the validity of the method. The performance exhibits the sophisticated property (accuracy and matching speed) of the proposed method. It is worth mentioning that this simple method has a recognition rate of close to 100% on the self-built dataset and has achieved excellent results for other datasets compared with some international state-of-the-art methods.
Formation of Innovative STEM-education

The article is devoted to the topical issue of forming a new direction of innovative STEM education. The purpose of the study is to analyze the essence and content of STEM education, identify the main problems and contradictions, and identify the main approaches to its development. To solve the tasks set, a set of theoretical methods corresponding to the subject of research and the goal was used. The theoretical and methodological basis of the study was system competencies and personal and activity approaches. A number of important problems and contradictions in the implementation of STEM education are identified, for example, the traditional education system does not fully meet the requirements and requirements of training and training of the workforce of the XXI century; difficulties in teaching subjects using the STEM method; low level of academic performance in subjects of physical and mathematical profile, as well as lack of ability to solve specific subjects. The article emphasizes the complexity and versatility of STEM education, as a result of which a wide variety of programs are being developed by type, direction and level of complexity to solve problems related to the lack of STEM literacy. A brief description of the main approaches to the development of these programs was given, and the main factors of reforming education in the STEM direction were identified. All the leading countries of the world were noted.
Research on Project-Based Learning Python Programming Course

In order to improve the teaching quality of Python programming course, this paper adopts the teaching model based on Project-Based Learning to design and decompose Student Information Management System, and reforms the teaching method, classroom teaching feedback and assessment methods in combination with the actual situation of the college. The practice results show that the teaching model based on Project-Based Learning can stimulate students' interest in learning, mobilize their initiative in learning, improve their engineering practice ability and programming ability, and achieve good teaching results.
Application Research of Advanced Intelligent Big Data Analysis Based on Intelligent Sensor Network in the Design of Personalized Education Management System and the Construction of Innovation System

With the continuous innovation and development of information technology and communication technology, human beings have entered a huge data center. In terms of big data, in-depth discussion on the in-depth application of big data is the current situation and development direction. Based on the intelligent sensor network, this paper conducts theoretical and empirical analysis combined with the application of individualized teaching in teaching and provides some reference opinions for the future development of individual pedagogy. On the basis of the research, the meaning of big data is explained from multiple perspectives. In the empirical research part, based on big data, based on real big data, and using relevant technologies in data mining, the relevance of each training program is analyzed. Mining was carried out using the FP-Growth method to analyze the correlation of each training scheme and optimize the FP-Growth method accordingly. The results of this experiment show that the use of big data can not only help teachers understand their students’ daily life, study habits, and consumption records but also make teachers’ work more dynamic, early warning, and subsidized. The research results are of great significance for guiding colleges and universities to formulate educational programs that are beneficial to students’ development, keep pace with the times, and cultivate modern educational technicians with modern significance.