Reviewerly/exHarmony huggingface dataset:
Development of a Training Robot for Slander Suppression

Slander on social networking sites has become a social problem, and measures, such as legislation and stricter regulations have been taken. However, there are limits to these measures, and we have not seen any system that encourages fundamental changes of the mind and behavior of contributors. Therefore, we propose a training robot that contributes to slander suppression, targeting people who post negative words regularly. To change his/her mind and behavior, the proposed robot system will serve as an intermediary to help people break out of the addiction of feeling good about slander by converting negative words in negative messages into positive words, generating positive messages, and presenting them to the user. In this report, we will describe the concept and the initial development of the proposed system to be implemented in a real robot system.
Towards Spatial Airflow Interaction: Schlieren Imaging for Augmented Reality

This work integrates Schlieren Imaging, a unique sensing modality, into Augmented Reality (AR) to explore ways to utilize invisible airflows for AR. Schlieren imaging is an imaging technique that visualizes the flow of fluids, which is normally invisible to the eyes. Theoretically, the technique can calculate the motion, pressure, temperature, and density of the airflow in our physical world. This unique, but less applied modality may expand interaction paradigms in AR and VR. We build a proof-of-concept AR system combined with Schlieren imaging that allows real airflow to affect virtual objects. The results of quantitative analyses show that our system can integrate different types of airflow with pressure values ranging from weak breathing actions to a heat gun up to 10m/s or 0.25m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> /min airflow. We also showcase AR use cases including blowing out a virtual candle and a heat gun.
A Human Activity Recognition Model Based on Wearable Sensor

With the development of sensor technology, wearable sensors are gradually applied to human activity recognition due to its advantages of improved performance and portability. By identifying different human activity, it has a wide application prospect in medical treatment, abnormal activity monitoring, interactive games, intelligent home and other aspects. This paper designs a human motion activity recognition model based on wearable sensor. Firstly, by preprocessing the data collected by the sensor, a subdivided activity dataset composed of eight kinds of activity is established (Sit, stand, walk slowly, walk briskly, go upstairs, go downstairs, jog and run briskly). Second, a hybrid deep network model based on convolution neural network and recurrent neural network is proposed. The feature of convolutional neural network and recurrent neural network is used to classify the subdivision activity data set and realize the recognition of specific activity. The results of recognition can provide scientific statistical data for real-time activity monitoring, activity warning and reminder, health report, user health information management and other applications.
A Deep Learning Framework with Transfer Learning Approach for Image Forgery Localization

Altering digital content is one of the major concerns experienced in the digital environment. Because of the developments in image modification applications in this digital era, the tampering of images for harmful reasons has proliferated, necessitating the detection of such manipulation before it misleads in any of the applications. A framework based on DeepLabv3+ is presented in this paper for detecting several forms of forgeries and locating the altered areas precisely within the images. To pre-process the forged photos, Error Level Analysis (ELA) is used, which differentiates the areas with varying quantization error levels. The complete framework is trained using the pre-processed images as input. The encoder side of the DeepLabv3+ architecture uses a pre-trained ResN et50 network for transfer learning. DeepLabv3+ uses Atrous convolution that helps to get the broader field of view and extracts contextual features for enhancing the precision of localization. At the decoder, the output features of the encoder are merged with the low-level features of the input image to appropriately locate the forged area. With high precision, this framework can identify and locate many types of manipulations. The findings illustrate that the proposed framework outperforms several existing forged area localization techniques.
Automatic Classification of Software Bug Reports Based on LDA and Word2Vec

Obtaining the types of software faults is of great significance for fault location and repair. Usually, manual classification methods are expensive. In this paper, we propose an automatic classification framework for Bug Reports based on LDA and Word2Vec. The feature representation of common classification methods has the problems of data sparsity and high dimensionality, so this paper adopts the feature representation method that combines LDA and Word2vec, which can represent words as low-dimensional word vectors with semantic relationships. Furthermore, to improve the quality of the classification model, we introduce the Self-Attention mechanism. The results show that the framework can automatically classify Bug Reports well.
Development of ACM International College Student Programming Competition System Based on Web

This paper introduces the design of a web-based am international college student programming competition teaching system. The system structure and page making of the main part are described in detail. The teaching system can facilitate students to learn am kcpc in Inernet environment and play a better guiding role.
Multi-Agent Distributed Cooperation Decision Making Based on Incomplete Information Prediction

In order to solve the problem of difficulty in learning cooperative strategies and decision-making of multi-agent caused by the incomplete information and the inability to guarantee stable communication in the partial observable multi-agent adversarial environment, inspired by the learning and reasoning functions of the human cerebral cortex through memory, a new multi-agent distributed cooperative strategy learning method based on incomplete information prediction in a partially observable adversarial environment is proposed. Through the use of historical memory and current observation information, support vector regression (SVR) is used to predict invisible information in the environment, and the prediction information of the unobserved part and the observed information are fused as the basis for cooperative strategy learning and decision-making. Then, through the use of decentralized multi-agent reinforcement learning method for cooperative strategy learning, the decision model of each agent in the team is obtained. The method is verified by multi-agent cooperative strategy learning and decision-making in a typical partial observable adversarial environment. The results show that this method can significantly improve the level of multi-agent cooperative decision-making while ensuring high prediction accuracy for unobserved parts.
Breast Cancer Risk Prediction with Stochastic Gradient Boosting

Breast cancer, which is an important public health problem worldwide, is one of the deadliest cancers in women.This study aims to classify open-access breast cancer data and identify important risk factors with the Stochastic Gradient Boosting Method.The open-access breast cancer dataset was used to construct a classification model in the study.Stochastic Gradient Boosting was used to classify the disease.Balanced accuracy, accuracy, sensitivity, specificity, and positive/negative predictive values were evaluated for model performance.The accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score metrics obtained with the Stochastic Gradient Boosting model were 100 %, 100 %, 100 %, 100 %, 100 %, and 100 %, and 100 % respectively.In addition, the importance of the variables obtained, the most important risk factors for breast cancer were a cave.points_mean, area_worst, and perimeter_worst, concave.points_worst respectively.According to the study results, with the machine-learning model Stochastic Gradient Boosting used, patients with and without breast cancer were classified with high accuracy, and the importance of the variables related to cancer status was determined.Factors with high variable importance can be considered potential risk factors associated with cancer status and can play an essential role in disease diagnosis.
Weak-supervised Visual Geo-localization via Attention-based Knowledge Distillation

Visual geo-localization aims to estimate the geo-graphical location of a query image by identifying the best-matched reference image from a GPS-tagged database. It remains a challenging task because of image appearance changes such as lighting, scale and pose. The current approaches do not have satisfactory performance for large-scale environments owing to the lack of learning discriminative features for image matching. To address the above problem, we introduce a practical method to exploit a weak-supervised model with selective transfer for feature distillation. We propose an image matching method that uses image sub-regions to adequately analyze the potential of difficult positive images. For improving the network generations and performance, the model estimates image-to-region similarity labels at no additional parameters or manual annotations by use of soft-labeled loss. Moreover, to have optimal training we propose a novel knowledge distillation (KD) method to effectively capture and transfer knowledge of a teacher network to a student network. More specifically, our method uses an attention network to learn relative similarities within features and utilizes these similarities to enhance the distillation intensities by further exploring the potential of difficult positive images. Our model achieves significant localization performance over large variations of appearance on three challenging datasets with satisfactory efficiency. Our code is available at https://github.com/XuYifan98/WAKD.
Uso de metodologías activas para el desarrollo de pensamiento crítico

The objective of this bibliographic research was to analyze research results on the use of active methodologies and the development of critical thinking in students, based on scientific production published in the databases of Scopus, Web of Science, Scielo and Latindex in the period 2016 -2021. To show that the use of these methodologies favors the development of critical thinking in students. The methodology used was the bibliographic review taking into consideration the objective, sample size, methodology and the results that show the relationship between the use of methodologies and the development of critical thinking in students of any level. It was concluded that scientific production regarding the use of active methodologies and their relationship with critical thinking has shown that there is a direct relationship, which implies that teachers should be trained in this methodology to ensure its use and proper application.
The Information Content of Blockchain Fees

Trading at decentralized exchanges (DEXs) requires traders to bid blockchain fees to determine the execution priority of their orders. We employ a structural vector-autoregressive (structural VAR) model to provide evidence that DEX trades with high fees not only reveal more private information, but also respond more to public price innovations on centralized exchanges (CEXs), contributing to price discovery. Using a unique dataset of Ethereum mempool orders, we further demonstrate that high fees do not result from traders competing with each other on private or public information. Rather, our analysis lends support to the hypothesis that they bid high fees to reduce the execution risk of their orders due to blockchain congestion.
Assessing the Impact of Major Political and Institutional Factors on New Businesses: The Case of Qatar

This chapter provides an assessment of the impact of the major political and institutional factors on establishing new businesses in the State of Qatar in the long run using the autoregressive distributed lag (ARDL) model. In particular, we are interested in studying the following political and institutional factors: political stability, rule of law, regulatory quality, control of corruption, and voice and accountability on new businesses registered. Empirical results indicate that political stability is found to have the strongest positive and significant impact among all other political and institutional variables, followed by rule of law, and control of corruption, while regulatory quality and voice and accountability are found to have an insignificant effect on new businesses registered. The outcome of this study helps policymakers in Qatar in figuring out the main political and institutional policies that need improvement to further develop and promote the private sector.
Activity Based Remote Health Monitoring System

Healthcare sectors are majorly moving towards Remote Health Monitoring Systems (RHMS) after the COVID-19 pandemic outbreak across the world. RHMS involves monitoring the patient’s vital parameters remotely and providing advice and consultation online. Alerts are generated whenever a particular health parameter exceeds the threshold and sent to the medical officers for further actions. However, it is observed that these thresholds are applicable only when a patient is at rest and can change drastically during patient’s physical activity such as walking, climbing the staircase, during exercise etc., which can mislead in understanding the patient’s health condition. Hence there is a requirement to correlate these parameter values with the current activity the patient is in and to generate activity-based dynamic thresholds. In this paper, a method to correlate the sensor values with physical activities is proposed. The activity-based RHMS (aRHMS) uses the motion sensors available in the patient’s smartphone to predict the activity and will automatically adjust the threshold values in co-relation with the activities and provides alarms/alerts accordingly.
Video-based Person re-identification with parallel correction and fusion of pedestrian area features

Deep learning has provided powerful support for person re-identification (person re-id) over the years, and superior performance has been achieved by state-of-the-art. While under practical application scenarios such as public monitoring, the cameras' resolutions are usually 720p, the captured pedestrian areas tend to be closer to 128×64 small pixel size. Research on person re-id at 128×64 small pixel size is limited by less effective pixel information. The frame image qualities are degraded and inter-frame information complementation requires a more careful selection of beneficial frames. Meanwhile, there are various large differences in person images, such as misalignment and image noise, which are harder to distinguish from person information at the small size, and eliminating a specific sub-variance is still not robust enough. The Person Feature Correction and Fusion Network (FCFNet) proposed in this paper introduces three sub-modules, which strive to extract discriminate video-level features from the perspectives of "using complementary valid information between frames" and "correcting large variances of person features". The inter-frame attention mechanism is introduced through frame quality assessment, guiding informative features to dominate the fusion process and generating a preliminary frame quality score to filter low-quality frames. Two other feature correction modules are fitted to optimize the model's ability to perceive information from small-sized images. The experiments on four benchmark datasets confirm the effectiveness of FCFNet.
An Efficient Trajectory-Planning Method With a Reconfigurable Model for Any Tractor-Trailer Vehicle

Trajectory planning for a tractor-trailer vehicle (TTV) is a tough task even in a static environment because of its complicated kinematic constraints. This article aims to efficiently plan an optimal trajectory with a reconfigurable model for any TTV in the static environment. A reconfigurable modeling method is first proposed to simulate the TTV with an arbitrary number of trailers and hitching mode. Then, the planning task is formulated as a nonlinear optimal control problem. However, it is too difficult to be solved and is time-consuming to find a feasible solution, let alone the optimal one. To this end, a novel guided solving method (GSM) is proposed to solve this problem progressively with great efficiency. GSM is divided into two steps. Step 1 plans rough trajectories for the TTV, taken as an initial guess for the next step. In step 2, the problem is split into a sequence of subproblems, solved by asynchronously expanding the size of both vehicle body and obstacles, speeding up the optimization process. Simulations are carried out in several complex cases, and four competitive algorithms are selected for comparison. The results show that the proposed method outperforms others in the feasibility and efficiency of problem-solving for trajectory planning. Specifically, the proposed method is feasible in all testing cases and saves up to 81.4%, 83.2%, and 55.9% solving time compared with other three methods in the 135° turn scenario, bringing the great improvement of planning efficiency.
Multi-concept Mining for Video Captioning Based on Multiple Tasks

Video captioning is a challenging cross-modal task that requires taking full advantage of both vision and language. To identify objects in videos, object detectors are usually employed to extract high-level object-related features, but the fine-grained knowledge from the object detectors are often neglected. Also, there is a fact that not just the task of object detection has the ability to obtain additional knowledge for video understanding. In this paper, multiple tasks are assigned to fully mine multi-concept knowledge in both vision and language, including video-to-video knowledge, video-to-text knowledge and text-to-text knowledge. Moreover, since there is a strong synergy in knowledge, both of global and local word similarities are developed based on the text-to-text knowledge to boost the robustness of the mined semantic knowledge. The mined knowledge can offer the model an extra guidance apart from linguistic prior to generate more semantically appropriate and grammatically correct sentences. The experimental results on the benchmark MSVD and MSR-VTT datasets show that the proposed method makes remarkable improvement on all metrics on MSVD and two out of four metrics on MSR-VTT.
Efficient and Secure Collaborative Processing in Mobile Edge Computing via Blockchain

Mobile edge computing (MEC) provides resource-hungry, energy-efficient, latency-sensitive, and privacy-secure computing services close to end-users, whereas the services are usually restricted to a single network. Blockchain emerges as a promising way to establish cooperative trust among MEC networks and enable large-scale and trusted cross-network edge cooperation on task processing. Meanwhile, integrating blockchain and cross-network edge collaboration faces a wide range of efficiency and security challenges. In this work, we propose a blockchain-native edge collaboration architecture to support trusted network information sharing, enable cross-network task distribution, and safeguard data processing in a collaborative cluster. We design a two-layer efficient permissioned consensus protocol dedicated to securing, validating, and monitoring the cross-network edge collaborations. Finally, comprehensive simulations are conducted to illustrate the effectiveness of the proposed architecture.
VisQuiz: Exploring Feedback Mechanisms to Improve Graphical Perception

In this paper, we explore the design and evaluation of feedback for graphical perception tasks, called VisQuiz. Using a quiz-like metaphor, we design feedback for a typical visualization comparison experiment, showing participants their answer alongside the correct answer in an animated sequence in each trial, as well as summary feedback at the end of trial sections. To evaluate VisQuiz, we conduct a between-subjects experiment, including three stages of 40 trials each with a control condition that included only summary feedback. Results from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n=80$</tex> participants show that once participants started receiving trial feedback (Stage 2) they performed significantly better with bubble charts than those in the control condition. This effect carried over when feedback was removed (Stage 3). Results also suggest an overall trend of improved performance due to feedback. We discuss these findings in the context of other visualization literacy efforts, and possible future work at the intersection of visualization, feedback, and learning. Experiment data and analysis scripts are available at the following repository https://osf.io/jys5d/
Multivariate Linear Regression

Regression methods are perhaps the most widely used statistical tools in data analysis. When several response variables are studied simultaneously, we are in the sphere of multivariate regression. The usual description of the multivariate regression model, which relates the set of m multiple responses to a set of n predictor variables, assumes implicitly that the m × n regression coefficient matrix is of full rank. It can then be demonstrated that the simultaneous estimation of the elements of the coefficient matrix, by least squares or maximum likelihood estimation methods, yields the same results as a set of m multiple regressions, where each of the m individual response variables is regressed separately on the predictor variables.
Material Meaning: An Investigation into How Meaning Is Organised and Interpreted in Industrial Design Practice

This paper reviews a pilot interview method, intended to investigate what meaning making is in industrial design practice. It is the second part of a PhD study on how meaning is organised in industrial design practice, and after design, when the product has transitioned into the possession of a user. Key literature on design, meaning, and practice is briefly considered, followed by a review of 3 semi-structured interviews with industrial designers, focused on discussing firstly a product they have designed, and secondly an object they own. The research takes a material centric approach, tracing the journey of the artefacts, rather than the designer. The artifacts become the focus for discussion of designers’ meaning making practices, and sketching is incorporated as an embodied method to get at a more layered, complex picture. Following the objects helps to ground reflection in the objects’ materiality, and dwell in the connections and relationships.