Reviewerly/exHarmony huggingface dataset:
Augmenting Feature Importance Analysis: How Color and Size Can Affect Context-Aware AR Explanation Visualizations?

Augmented Reality (AR) has shown significant potential in supporting in-situ decision-making in various application areas. Many prior works have shown how AR can visualize the decision support data in various contexts. However, prior research about AR-based decision support systems rarely explored how the explanations were visualized. Providing context-aware explanations within AR-based recommendation systems may help users instantly understand the recommendations they have been given. Therefore, this paper presents the world-first user study exploring AR explanation visualization designs. Three feature importance analysis visualizations that apply different color-coding and size-scaling strategies were designed to explain the recommendations provided by a context-aware AR shopping assistant system. Twenty-four participants were recruited to evaluate these three explanations in a shopping scenario. The results revealed novel findings that could help guide the appropriate utilization of descriptive parameters when designing AR explanation artifacts. The results also show the potential of providing intuitive visualization to explain recommendations in AR.
Side Channel Attack on SM4 Algorithm with Deep Learning-Based Analysis

SM4 is a Chinese symmetric algorithm, which is a little bit similar to AES. If the key of the algorithm is recovered, adversaries can easily make a fake message by replacing part of the ciphertext. In addition, adversaries can learn the secret message just by decrypt the ciphertext he got. Thus, it is critical to recover the secrete key. Gennerally, there are several ways for choice, like the traditional side channel analysis (SCA) and the template attack-based way to achieve recovering the target key. We introduce a new way to recover the secrete key which innovatiely use the technique of deep learning. We use the training set to train the network for specific epoches firstly, and use the well trained model to attack the target dataset and the result shows that this method can work successfully.
Latency Analysis for Raft Consensus on Hyperledger Fabric

As a permissioned blockchain system, Hyperledger Fabric is getting increasing attention from enterprises, however, the current Hyperledger Fabric system is difficult to meet the real-time application requirements of enterprises. This paper focus on the Raft consensus latency that is time consumption of consensus, we provide a theoretical model of the Raft consensus latency. Subsequently, we analyze the effect of consensus latency on the transaction latency and the effect of a block size on consensus latency by extensive experiments. The experimental result shows that the proportion of consensus latency in the entire transaction latency is relatively small, while the block size has more greater effect on the consensus latency.
Copy-Move Forgery Detection-A Study and the Survey

The issues caused due to image manipulations are common these days. Furthermore, it causes severe troubles in news broadcasting, social media, and even in digital media forensics. Mainly, the types of image manipulations can be divided into four; they are splice forgery, copy-move, morphing, and retouching. Among these 4 types, copy move forgery is challenging to detect since the image characteristics is not changing even after the forgery. Also, the manipulated images can undergo attacks including rotation, scaling and distortion making it even hard to detect the forgery. In this survey paper, we have reviewed the types of image manipulations and elaborated on copy-move forgery detection and the challenges faced in copy-move forgery detection. The paper has reviewed seminal copy-move forgery-related research articles published between the year 2011-2022.
Improving Employee Performance Affected by Leadership Style, Organizational Culture, and Work Ethics in Technology Service Company

This study analyzes the increase in employee performance which is influenced by leadership style, organizational culture, and work ethics.The object of this research is one of the finance companies that provide consumer credit to the public.The number of respondents is 100.The sampling technique used is a stratified approach.Partial Least Square method is used in analyzing the relationship between variables.The results showed that leadership style, organizational culture, and work ethics had a significant effect on employee performance.Work ethics has the highest implications for improving employee performance.A work ethic that is carried out professionally, full of integrity, and perseverance will result in the achievement of maximum performance.Companies must anticipate business changes through leadership style and organizational culture.A leader must have a style in dealing with various conditions.Organizations must ensure that culture becomes a role model for employees in producing quality business processes.These two variables still have an important role in improving employee performance.
Attentional Factorized Q-Learning for Many-Agent Learning

The difficulty of Multi-Agent Reinforcement Learning (MARL) increases with the growing number of agents in system. The value function decomposition is an effective way to alleviate the curse of dimension. However, the existing methods usually either can only provide the low-order approximate decomposition of no more than the second-order, or need to spend a lot of effort manually designing the high-order interaction among agents according to experience. Therefore, the existing methods either tend to bear large decomposition error or are not convenient to use. In this paper, a high-order approximate value function decomposition method is proposed, which has the following prominent characteristics: the low-rank vector is exploited to represent value function, the low-order and high-order components share the same input (i.e., the embedding vector), the attention mechanism is used to select the agents participating in the high-order interaction, and all agents share the model parameters if the agents are homogeneous. To our knowledge, this is the first MARL method modeling low- and high-order interaction simultaneously among agents that can be trained end-to-end. Extensive experiments on two different multi-agent problems demonstrate the performance gain of our proposed approach in comparison with strong baselines, particularly when there are a large number of agents.
Crowd Counting based on Density Map Dynamic Refinement

For crowd counting, the existing methods usually use an end-to-end approach to directly output the final estimated density map and perform the counts. However, as an intermediate representation, the quality of the estimated density map may significantly affect the counting performance. Therefore, some studies have attempted to optimize the estimated density map with additional attention mechanism. But these methods only focus on the high-density crowd areas and ignore the optimization of local detail areas. Consequently, we propose a more intuitive and understandable Density Map Dynamic Refinement Network (DDRNet) consisting of Counter and Refiner to further refine the local detail information of the estimated density map. Our training contains two stages. Specifically, for the first stage, Counter generates the initial density map through the feature extraction module and the backend, while Refiner, which consists of convolutional layers with different dilated rates, further refines the output of the former to obtain the final estimated density map in the second stage. Also, due to the different views of Counter and Refiner during training, we design a dynamic joint training strategy to improve counting performance. Extensive experiments on three crowd counting datasets (ShanghaiTech, UCF_CC_50, UCF-QNRF) demonstrate the effectiveness of the proposed model and achieve superior counting results.
Conceptual forms of knowledge and intentiality of typological reflection

Purpose of the study is substantiate the relationship between conceptual forms of cognition and the intentionality of typological reflection as the basis of the subject-semantic sphere of personality. The novelty of the work lies in the identification of the epistemological component of the typological potential of the individual's reflection, which is aimed at improving the epistemological experience of practical activity. In the aspect of intellectual synthesis, the objectivity of reflection as a typological-cognitive factor of meaning-forming characteristic has been proved. As a result of the study, the communicative-pragmatic essence of epistemological experience is concretized as the basis of the epistemological construct of the unity of conceptual forms of cognition and typological reflection in the structure of the activity of the subject of sociality.
A Pose-Aware Global Representation Network for Human Parsing

Many recognition tasks including image/video classification, segmentation and object detection can be improved by the integration of global information. Although global information may be better represented in some recognition tasks than the others, it is worth exploring how global information from related tasks can be effectively used to improve the performance of a target task. The task of pose estimation predicts the locations of human joints, thus providing global information about the human body. In this paper, we propose a pose-aware global representation network model (PAGRnet) that exploits global information from pose estimation to enhance feature learning in human parsing. In our PAGRnet model, a novel learning module with three integrated parts is used to learn global information. The first part generates a global joint representation, while the second part learns the relationship between the pixels and joints. By integrating the global joint representation with the pixel-joint relationship, the resulting pose-aware global representation is augmented for the parsing task. Our experimental results show competitive performance of our method on the LIP, the Pascal-person-part and the ATR datasets, with reduced computation costs in comparison to other proposals of global information fusion. We also demonstrate the advantages of our feature fusion model over concatenation, pixel-wise and channel-wise relation models.
An Analysis of Various Algorithmic Behaviors in Detecting a Financial Fraud

The most interesting thing about occurrences of fraud is that they tend to have a pattern. Though nonsingular or linear, the patterns can be very divergent and have a wide leap of repetition. In recent decades, there’s been a growing reliance on cloud and mobile computing, and though there’s security, frauds tend to evolve with new patterns. Developing a fast-paced identifier of these patterns tends to help in detecting fraud beforehand with higher accuracy. The project is an analysis of how different algorithms and their combinations behave while detecting fraud. We used an Automobile insurance claim data-set operated under various algorithms like XG-Boost, Linear Discriminant Analysis, Gradient Boosting Classifier, Bernoulli NB, Bagging Classifier, Decision Tree Classifier, Ada-Boost Classifier, Random Forest Algorithm, K Neighbours Classifier, Support Vector Machine, and Logistic Regression Algorithm. The model responds better with XG-Boost followed by Linear Discriminant Analysis. This could be paired up with the idea that gradient-based algorithms work faster and are more accurate and can be integrated with an improvised model sequencing the algorithms that predict fraud with minimum fallback.
Research on Pet Recognition Algorithm With Dual Attention GhostNet-SSD and Edge Devices

The ability to identify pets via smart home technology is crucial for managing pet health. However, the present popular object detection method model is too large, support for edge devices is insufficient, and performance is subpar for small object identification in the traditional algorithm, such as SSD. In this study, we propose an improved SSD object detection network algorithm based on a GhostNet network with a dual attention mechanism. A ghost module is introduced to create a lightweight ghost network based on the SSD network. The ghost module is combined with the ECA attention mechanism to dynamically allocate parameters to the target and alter the detection region weights to enhance the model performance. In order to reinforce the model and increase the precision of the small target, the CBAM module was also introduced. It assigns major weights to the output target regions of the large-scale feature layer. According to the experimental findings, the CBAM-GhostNet-SSD network significantly reduces the number of backbone parameters and the calculation amount is reduced by 98.23% when compared to the classic SSD. This lays the groundwork for the self-developed algorithm’s successful deployment of edge devices. The predicted rate synchronization increased by 3.1 times, also mAP was 14.5% higher than SSD. The lightweight model quantitative transformation utilized in edge equipment can accurately evaluate the target region and realize dynamic detection. This has a certain guiding significance for the subsequent detection of household pet targets.
Evaluating the Impact of OCR Quality on Short Texts Classification Task

The majority of text classification algorithms have been developed and evaluated for texts written by humans and originated in text mode. However, in the contemporary world with an abundance of smartphones and readily available cameras, the ever-increasing amount of textual information comes from the text captured on photographed objects such as road and business signs, product labels and price tags, random phrases on t-shirts, the list can be infinite. One way to process such information is to pass an image with a text in it through an Optical Character Recognition (OCR) processor and then apply a natural language processing (NLP) system to that text. However, OCR text is not quite equivalent to the ‘natural’ language or human-written text because spelling errors are not the same as those usually committed by humans. Implying that the distribution of human errors is different from the distribution of OCR errors, we compare how much and how it affects the classifiers. We focus on deterministic classifiers such as fuzzy search as well as on the popular Neural Network based classifiers including CNN, BERT, and RoBERTa. We discovered that applying spell corrector on OCRed text increases F1 score by 4% for CNN and by 2% for BERT.
CVallet: A Blockchain-Oriented Application Development for Education and Recruitment

The application of blockchain technology in education has gained increased attention from researchers and practitioners in the past few years. However, most pilots are stuck in the prototype stage and meet industrial adoption barriers. In this study, targeting the identified hindering factors, we develop an education credentials management and verification DApp to improve the adoption potentiality of blockchain through methodological, social and industrial aspects. Firstly, we innovatively incorporate a co-design method based on blockchain-oriented software engineering (BOSE), which may bring new implications for future studies of user-centred DApp development methods. Through intensive research of user experiences, not only do we improve the social awareness and knowledge of this new technology but we also develop more precise functional goals that can solve actual industrial pain points. Finally, our proposed prototype makes improvements in terms of architecture modular design, selection of blockchain, and leverage of interoperability-supported tools with the expectation to improve the application adoption further, particularly in the education and recruitment sectors.
Cultural and Educational Mission of the University Library: Project Approach

The introduction of the project approach as a tool for the development of cultural and educational activities of university libraries is an urgent topic against the background of trends of increasing public and scientific attention to the “third mission” of the university as a cultural center of the region. The problem lies in the fact that libraries of higher educational institutions as scientific libraries pay priority attention to the promotion of research activities and the educational process at the university, and the potential of project cultural and educational activities in comparison with public libraries is not fully used. The purpose of the article is to summarize the experience of implementing cultural and educational projects in the university library. The experience of the Fundamental Library of Tambov State University named after G.R. Derzhavin shows that the use of the project approach allows you to gradually concentrate efforts on the implementation of socially significant cultural initiatives, and, following the ideology of the “entrepreneurial university”, to receive additional financial support for projects through participation in grant competitions. As conditions for the successful implementation of cultural and educational projects, the involvement of specialized departments as internal partners and the formation of creative competencies among library staff are highlighted.
Welcome to the Sixth International Workshop on Crowd-Based Requirements Engineering (CrowdRE’22)

Welcome to the 6th International Workshop on Crowd-Based Requirements Engineering (CrowdRE’22), where scientists and representatives of industry meet to exchange ideas, experiences, and other contributions regarding the state of the art of Crowd-Based Requirements Engineering (CrowdRE). The discipline of CrowdRE seeks to address the challenges of traditional requirements engineering (RE) in scaling up to settings with thousands to millions of users of (software) products or (software-driven) services, who form a large and heterogeneous group that can be denoted as a ‘crowd’ [1], [2]. The user feedback generated by the crowd, such as texts or usage data, can be a valuable source of requirements, problems, wishes, and needs. Responding quickly, effectively, and iteratively to this feedback can greatly increase a product’s success. CrowdRE comprises any approach that provides RE with suitable means for this crowd paradigm, especially by involving the crowd and by collecting, harmonizing, analyzing, and interpreting their user feedback.
Intrinsic Reward with Peer Incentives for Cooperative Multi-Agent Reinforcement Learning

In this paper, we propose a novel Intrinsic Reward method with Peer Incentives (IRPI) to promote the inter-agent direct interactions and implicitly address the credit assignment problem in cooperative multi-agent reinforcement learning (MARL). The IRPI method can build mutual incentives between agents by using their causal effect, to realize their advanced cooperation. Specifically, a new intrinsic reward mechanism is conducted, which equips each agent with the ability to reward other agent by using the causal effect between them. Moreover, the mechanism is built through a neural network and learned by using causal effect between the agents. Furthermore, the counterfactual reasoning is used to infer the causal effect between the agents using the joint action-state value function, and then assess the quality of the effect using individual state value function in MARL. Simulational results in Starcraft II Micromanagement demonstrate that the proposed IRPI can enhance cooperation among the RL agents to achieve better performance than some state-of-the-art MARL methods in various cooperative multi-aaent tasks.
Importance of stochastic components for Ising Hamiltonian-based invertible logic

This work is a case study of synthesizing networks of binary stochastic units to perform Boolean logic functions and their inverse based on the intrinsic minimization of a Hamiltonian energy function. In particular, we shed light and provide some intuition on the role of stochasticity and the ability to adjust the network temperature in generating correct results, at various functional complexity levels.
Computation of Solutions to Certain Nonlinear Systems of Fuzzy Relation Inequations

Although fuzzy relation equations and inequations have a broad field of application, it is common that they have no solutions or have only the trivial solution. Therefore, it is desirable to study new types of fuzzy relation inequations similar to the well-studied ones and with nontrivial solutions. This paper studies fuzzy relation inequations that include the degree of subsethood and the degree of equality of fuzzy sets. We provide formulae for determining the greatest solutions to systems of such fuzzy relation inequations. We provide alternative ways to compute these solutions when we cannot run the methods based on these formulae.
An Experimental Platform for Autonomous Intelligent Cyber-Defense Agents: Towards a collaborative community approach (WIPP)

Cyber defenses are increasingly challenged to keep up with attackers who might be using automation or machine learning as part of their attack strategies. Autonomous defensive systems will become critical in the coming years to respond to this threat. We present an update on ongoing work to build an autonomous intelligent cyber-defense agent. An initial prototype was demonstrated to NATO ACT in the fall of 2021, and work continues to add additional capabilities to it. The agent in its current state is capable of simple, statically-configured responsive actions as well as storing several types of observations (network scans, IDS alerts, Netflow data) in a knowledge graph. We outline the background for this work, detail the demonstration scenario used last fall, progress since then, and a future roadmap and collaborative strategy for this project including a reinforcement learning approach to automated reasoning.
Spatial-Semantic Collaborative Graph Network for Textbook Question Answering

Textbook Question Answering (TQA) task requires answering questions by reasoning based on both the given diagrams and text context. There are mainly two challenges for the task. First, the diagrams are different from the natural images. Similar shapes or color blocks may express different semantics and there is also a large intra-topic variation for diagrams. Hence, the characteristics of visual semantic ambiguity and variable visual appearance make the diagram understanding more challenging. Second, for the text, the specific education domain with terminologies exists a great gap with the general domain. Therefore, it is difficult to represent the text semantics effectively using a text encoder pretrained in the general domain. In this paper, we propose a Spatial-Semantic Collaborative Graph Network (SSCGN) for TQA task, which can help enhance the diagram and text understanding and facilitate multimodal reasoning. Specifically, the Spatial-guided Semantic Enhancing (SSE) module fully exploits the spatial and semantic relationships between visual objects and OCR tokens to collaboratively enhance the diagram semantic understanding. Moreover, based on the semantically enhanced region representations of the SSE module, the Fine-grained Spatial-Aware Graph Network (FSA-GN) can help obtain richer relation-aware region representations for joint reasoning by capturing more fine-grained spatial relationships. We further propose multiple self-supervised auxiliary tasks to enhance the initial diagram and text semantic representations by pretraining the diagram encoder and text encoder. Extensive experiments and ablation studies are conducted to validate the effectiveness of SSCGN.