Reviewerly/exHarmony huggingface dataset:
Learning Fashion Compatibility from In-the-wild Images

Complementary fashion recommendation aims at identifying items from different categories (e.g. shirt, footwear, etc.) that "go well together" as an outfit. Most existing approaches learn representations for this task using labeled outfit datasets containing manually curated compatible item combinations. In this work, we propose to learn representations for compatibility prediction from in-the-wild street fashion images through self-supervised learning by leveraging the fact that people often wear compatible outfits. Our pretext task is formulated such that the representations of different items worn by the same person are closer compared to those worn by other people. Additionally, to reduce the domain gap between in-the-wild and catalog images during inference, we introduce an adversarial loss that minimizes the difference in feature distribution between the two domains. We conduct our experiments on two popular fashion compatibility benchmarks - Polyvore and Polyvore-Disjoint outfits, and outperform existing self-supervised approaches, particularly significant in cross-dataset setting where training and testing images are from different sources.
A Fine-Grained Anomaly Detection Method Fusing Isolation Forest and Knowledge Graph Reasoning

Anomaly detection aims to find outliers data that do not conform to expected behaviors in a specific scenario, which is indispensable and critical in current safety environments related studies. However, when performing outlier detection on a large-scale multidimensional dataset, most of the traditional methods make no distinctions between classes of outliers and lack the reasoning ability and the explainability of the analysis results, which leads to the low accuracy of global outlier detection and the inability to effectively identify local outliers. In this paper, we propose a fine-grained anomaly detection method which well utilizes isolation forest and knowledge graph reasoning tactics. First, a better extracting data feature method and a more reasonable weighted strategy are used to find global outliers based on the traditional isolation forest algorithm, and then we construct a custom rule base according to the in-depth research and analysis of global abnormal data, and at last the ontology knowledge is reasoned based on such rule base to achieve the detection of local outliers. Through analysis of extensive experiment results, our method could effectively discover more abnormal data without big loss of time costs, has strong generalization, and correspondingly improves the performance for abnormal detection.
Multimedia Application for Analyzing Interdisciplinary Scientific Collaboration

Information about scientific research can be represented on interactive science maps, which can yield insight into scientific activity at different levels of organization: micro, meso and macro. There are many examples at the meso or macro levels, i.e., research fields. But the micro level – individual analysis - is rather rarely considered in academic writing or app design. The authors designed and tested a web application, Scientific Visualizer, to illustrate an individual's scientific activity, which became a basis for a wide-ranging analysis. The visualizations generated by software will aid researchers in management of their own career, as well as planning effective communication and collaboration. The authors also have discussed their own approach to constructing a disciplinary space of publications based on the current classification of science. They added to scholarship on software and its application in academic community.
Exploring Features of the Design on Futures Thinking

Nowadays, with products and services getting more intelligent and evolving faster, the human-centred design methods, such as Design Thinking, have presented some areas that they can’t cover. Design Thinking takes the rapid feedbacks of users as the substitute for subjective speculation and long-term consideration of designers, which makes the design suitable for solving current problems but relatively short-sighted. In this context, some researchers have begun to explore ways to map out a more permanent, constant values behind products and services, to plan a longer vision of design. In many explorations, we found the concept and method of Futures Thinking to inspire Design for farsighted. With it we can explore and enlarge the future possibilities of design, make design adapt to various futures. On this basis, we carried out researches on what kind of design can reflect the characteristics of Futures Thinking. And, this paper focuses on pointing out the features of this type of design. Through interviews and text deconstruction and reconstruction, this paper summarizes the keywords of the design features on Futures Thinking. It is hoped that the outline and overview of such type of design can contribute to the discussion of design on Futures thinking, so that more people can participate in the dialogue and discussion about the futures, to design the preferable futures.
Dialogue Management as Graph Transformations

We present ongoing work on a new dialogue management framework using graphs as core representation for the current dialogue state. Dialogue management tasks such as state tracking and action selection are framed as sequences of graph transformations that repeatedly update this graph based on incoming observations. Those graph transformations are expressed using a graph query language, making it possible to specify all dialogue management operations through a unified, declarative syntax. We argue that graphs are particularly well suited to model the dialogue state of complex, open-ended domains. In contrast to traditional dialogue state representations that are limited to fixed, predefined slots, graphs can naturally express dialogue domains with rich relational structures and variable numbers of entities to track. We describe how dialogue state tracking and action selection can be modelled in such graph-centric view of dialogue management, using either handcrafted rules or data-driven models. We also briefly discuss how to account for some aspects of dialogue management such as uncertainties, incremental inputs and contextual knowledge. Finally, we describe a proof-of-concept study of this dialogue management framework in a human–robot interaction scenario.
Reducing the dimension of the feature space for the classification problem using a genetic programming algorithm

Often, a decrease in the efficiency of machine learning algorithms is associated with the problem of classification quality on data with a large dimension of the feature space. Therefore, for data mining methods in solving classification problems, finding the optimal feature space is an important task. A method is proposed to dimensionality reduction of the feature space by constructing new features (a new feature space) using a genetic programming algorithm. The dimension of the space to which the source data needs to be compressed can be set at will. After dimensionality reduction, the accuracy of classification on the new feature space has almost minimal differences with respect to the accuracy of classification on the original feature space. In addition, the sample distribution of the original feature space is almost statistically not significantly different from the constructed one. The approbation of the obtained approach is presented on a representative set of test and practical tasks.
Research on fingertip positioning and human-computer interaction technology based on stereo vision

Human-computer interaction system based on computer vision is an important research direction in the field of human-computer interaction, which has broad application prospects in natural human-computer interaction, sign language recognition, virtual reality, smart home, somatosensory games and other fields. In this paper, an interactive visual perception method is proposed, which uses human experience to guide the computer to quickly build the visual perception model. By analyzing the difference between the predicted image and the actual image read by the camera, the area with large reflectivity change on the projection screen is found as the user area. This method can find the position of real human hand correctly even if the projection image contains human hand. Three-dimensional coordinates of fingertips are obtained by stereo matching principle, and Kalman filtering tracking algorithm is used to smooth the trajectory of fingertips and narrow the detection range of the next frame.
Sample Reduction for Support Vector Data Description (SVDD) by Farthest Boundary Point Estimation (FBPE) using Gradients of Data Density

Classification is a quintessential application of machine learning for which support vector machines have been used ubiquitously because of their optimal margins and ease of use. However, they’re rarely used for large datasets due to the cubic time complexity of their training process. This has inspired several papers attempting to reduce the number of features or the number of training samples to lessen the training time of the SVMs. This paper aims to propose a novel approach for reducing the number of training samples for support vector data description (SVDD) while attempting to maximize the knowledge of the target class by selecting the most promising candidates for support vectors, which are the farthest boundary points of the data clusters. The proposed algorithm utilizes the density gradient across the data distribution to uniformly detect the boundary points, which are sampled as potential support vectors to train the support vector machines in a smaller amount of time without significant loss in accuracy. The proposed algorithm is verified via tests conducted on Human Activity Recognition, Breast Cancer Detection, and Heart Disease Detection Datasets.
Koopman pose predictions for temporally consistent human walking estimations

We tackle the problem of tracking the human lower body as an initial step toward an automatic motion assessment system for clinical mobility evaluation, using a multimodal system that combines Inertial Measurement Unit (IMU) data, RGB images, and point cloud depth measurements. This system applies the factor graph representation to an optimization problem that provides 3-D skeleton joint estimations. In this paper, we focus on improving the temporal consistency of the estimated human trajectories to greatly extend the range of operability of the depth sensor. More specifically, we introduce a new factor graph factor based on Koopman theory that embeds the nonlinear dynamics of several lower-limb movement activities. This factor performs a two-step process: first, a custom activity recognition module based on spatial temporal graph convolutional networks recognizes the walking activity; then, a Koopman pose prediction of the subsequent skeleton is used as an a priori estimation to drive the optimization problem toward more consistent results. We tested the performance of this module a dataset composed of multiple clinical lower-limb mobility tests, and we show that our approach reduces outliers on the skeleton form by almost 1 m, while preserving natural walking trajectories at depths up to more than 10 m.
Dictionary Based Gender Identification and Gender Based Sentiment Analysis with Polarized Word2Vec

Each gender is having its special behaviour which can be reflected in every field of social media. During the pandemic of COVID-19, people used twitter to discuss the issues caused by COVID-19 disease. As Twitter does not disclose the gender of the user, in this study we have discussed different kinds of approaches used to identify the gender. From the literature review, it is found that the dictionary-based approaches are the best suitable approach when we are working with the sentiment analysis of unlabelled data. This study is about the analysis of ten kinds of emotions of males and females by which we can observe how they reacted in this pandemic. The research proposes a dictionary-based approach to identify the gender and then analyzed sentiments using the cluster-based approach is applied onto word vectors after multiplying them with sentence’s polarity. The proposed approach is compared with the existing approaches with different data set and found that our proposed approach depicts good accuracy of sentiment analysis of unlabelled gendered data.
Design and Implementation of ROS-based 5G Operation and Maintenance Center Inspection Robot Navigation System

To realize the intelligent navigation and safe obstacle avoidance of the intelligent inspection robot in the complex environment of the 5G operation and maintenance centers, and build an accurate environment map, This paper designs an autonomous navigation system for 5G operation and maintenance center inspection robots based on the robot operating system ROS. During the navigation process of the inspection robot in a known environment, The A* algorithm with a new heuristic function is used for global path planning, and the improved DWA algorithm is used for local path planning. Based on the traditional DWA algorithm, a new distance evaluation function is added to optimize the obstacle avoidance effect of the inspection robot. It enables the robot to reach the target point faster and safer, and conduct related simulation experiments and real-environment experiments. The experimental results show that the method can realize the real-time dynamic obstacle avoidance of the robot in complex environments and reach the endpoint, and it can be closer to the global path planned by the A* algorithm. Guarantee the global optimal route.
Implementation and Evaluation of Exercise and Performance Learning Assistant System Platform for Yoga Pose Practices Using Node.js

To support people enjoying exercises or learning performances at home such as Yoga poses or traditional dances, we have developed Exercise and Performance Learning Assistant System (EPLAS). In EPLAS, the user can practice it by following the model performance of the instructor in the video. Then, the key poses of the user will be rated as the feedback by comparing the keypoints between the instructor and the user that are extracted by OpenPose. We have provided the platform using the web browser. However, OpenPose needs to run on OS directly. We developed the EPLAS platform in this paper using Node.js as the web application server. When a user requests it via the browser, the rating function is immediately executed on the server. Furthermore, we employ use Docker to simply distribute the platform. We invited 10 Okayama University students to do various Yoga positions using the EPLAS for assessments, and the results verified the success of the deployment.
Blockchain for Circular Economy in the Furniture Sector

The research aims at examining the potential use of blockchain technology in the furniture sector's supply chain. To better understand how the furniture sector is articulated, Cubo design S.r.l and its new 4.0 production plant are used as a case study. Cubo design is an SME from Abruzzo active in the manufacture of parts and accessories of furniture. The authors describe the switch the furniture sector has undergone from “old” handicraft production with a high presence of labor and with a significant presence of external suppliers and manual production methods to the new paradigm based on smart factories. From this point, they evaluate the possible and future adoption of blockchain technologies in terms of the benefits the technology could bring to the sector, how it can be combined with 4.0 logic, and what improvements it would create to support the furniture circular economy.
Industrial User Experience Index vs. Quality of Experience Models

Network operators have increased interest in understanding and quantifying the user perceived quality of experience (QoE). Appropriate QoE models allow mapping of measurable QoS parameters to QoE metrics like mean opinion scores (MOS) or poor-or-worse (PoW) ratios. Unfortunately, there are no QoE models for all applications available yet, and development and standardization is rather time-consuming. For that reason, industry often implements simple but effective approaches based on expert knowledge to get a user experience index able to deliver high value insights. This index is often defined based on thresholds of measurable QoS parameters to classify the resulting user experience. The question arises whether such a threshold-based user experience index can properly estimate the QoE in a system. We therefore answer in this work whether the user experience index is able to quantify the expected QoE in a system and if other QoE metrics like PoW ratios can be derived. Furthermore, we discuss the interpretation and the limits of the index. As a key contribution, we present a detailed systematic analysis of the threshold-based QoE concept and discuss its usability as QoE estimation methodology for industry.
Analysis of the Application of Communication Information Technology in Micro, Small and Medium Enterprises

Technology is something that cannot be separated from human life from small children to adults, including in the field of micro, small and medium enterprises of Indonesian society, this causes the need for surveys and analysis as well as examples of the application of communication technology in micro, small and medium business flows, by researching in the community about the application of technology in micro and small businesses using field survey methods and the data results will be displayed quantitatively, it is hoped that they will find needs that are in accordance with the interests and needs of micro and small business actors so that they can be more efficient in using information technology and communication.
Tutorial: Analyzing, Exploiting, and Patching Smart Contracts in Ethereum

Smart contracts are programs which encode business logic and execute on the blockchain. While Ethereum is the most popular blockchain platform for smart contracts, an increasing number of new blockchain platforms are also able to support smart contract execution (e.g., Solana or Cardano). Security vulnerabilities in Ethereum smart contracts have demonstrated that writing secure smart contracts is highly challenging. This is exacerbated by the fact that the exploitation of buggy smart contracts seems disproportionately easier compared to exploiting classic PC software. In this tutorial, we overview a number of smart contract vulnerabilities focusing on the Ethereum ecosystem. We also provide an introduction to the de-facto smart contract programming language Solidity and provide a comprehensive hands-on lab tutorial that involves analyzing vulnerable smart contracts, developing proof-of-concept exploits as well as introducing security analysis tools for testing smart contracts
Fraudulent Transactions Prediction Using Deep Neural Network

Today, data is increasingly easily accessible, with corporations storing information with high volume, variety, speed, and value. This data is derived from several sources, including social media and user purchase transactions. Payment transactions have swiftly proliferated in their various forms,including cash-in, cash-out, debit, payment, and transfer. According to this, one of the most important dangers to online security nowadays is fraudulent transactions. Suspicious trans-action monitoring should therefore be an essential component of any payment system. Fraud transactions can be detected by evaluating consumer habits from past transaction data. In this paper we are proposed a Neural Network model to detect different types of fraudulent transactions using a recent data set to cope the weakness of the previous works. AUC of 99% has been obtained by using the proposed model as observed in the experimental results.
Towards the Robustness of Multiple Object Tracking Systems

Due to the wide use of visual perception techniques in safety-critical fields, existing studies have tested the robustness of the essential object detection systems in scenarios with different image content. However, the applications that perceive one video with multiple image frames, such as autonomous driving, usually further require the trajectories of objects. This is mainly realized by combining detecting objects and associating detected objects in frames using multiple object tracking (MOT) systems. Thus, it is also essential to test the robustness of MOT systems, particularly in their exclusive scenarios that involve variety beyond the static image content. In this paper, we propose a novel testing method with five new Metamorphic Relations to realize the robustness test for MOT systems in two typical categories of scenarios, i.e., the speed variety of tracked objects and temporary camera failures. Our method also properly addresses the oracle problem and the lack of test cases for some rare scenarios to make the test efficient and diverse. Finally, we use our method to test three typical MOT systems and effectively reveal numerous and diverse MOT errors. We also extensively discuss the performance of tested systems and summarize two typical scenes where they often misbehave.
CCSBD: A Cost Control System Based on Blockchain and DRG Mechanism

Diagnosis Related Group (DRG) allows patients to be grouped according to the initial diagnosis and to be prepaid within the group. Actual costs in follow-up treatment cannot exceed the prepaid value, achieving the purpose of medical cost control. Three problems exist in the process. First, some treatment operations are highly overlapping and therefore cannot be accurately classified. Second, classification data cannot be credibly shared across hospitals. Third problem is the historical payment path required to predict costs cannot be fully traced. To address these problems, we design a Cost Control System Based on Blockchain and DRG Mechanism (CCSBD). We proposes a fusion classification model to realize the contribution assessment of the important feature factors, leading to accurate classification. In order to ensure the security and consistency of shared information, We establish a hyper-ledger blockchain architecture for secure sharing of medical data. Through smart contract, the architecture realizes dynamic consensus endorsement of data and cross-chain cross-authentication of departmental attributes. We realize value data screening and clinical path tracking through logical chain code to generate reasonable cost metrics to predict expenses, and implement CCSBD on the Fabric consortium-chain platform. Through comparative analysis with three single classification models, we prove that CCSBD improves classification accuracy by 7%. Furthermore, the security and efficiency of the shared structure are demonstrated by smart contract latency tests and consistency attack tests.
From Quantum to Classical Nonlinear Dynamics in the Unbalanced Dicke Model

We investigate the appearance of classical nonlinear dynamics in the non-equilibrium quantum phase transition to superradiance in the unbalanced Dicke model. Specifically, we uncover a variety of dynamical behavior, including hysteresis, superradiant oscillations, and chaos.
Active Learning for kNN Using Instance Impact

Labelling unlabeled data is a time-consuming and expensive process. Labelling initiatives should select samples that are likely to enhance the classification accuracy of the classifier. Several methods can be employed to accomplish this goal. One of these techniques is to select samples with the highest level of uncertainty in their predicted labels. Experts then label these samples. Another option is to choose samples at random. This paper proposes three methods for identifying unlabeled samples to improve predictive accuracy when they are labelled. Our study explores how to select samples when we have very few labelled samples available from manifold distributed data sets. In order to assess performance, we have compared our approaches with uncertainty sampling and random sampling. We demonstrate that our methods outperform uncertainty sampling and random sampling by using public and real-world data sets.