Reviewerly/exHarmony huggingface dataset:
Augmented Reality for Real-Time Control of a Robotic Arm with IoT Connection

Augmented Reality (AR) is one of the most important technologies that takes part in the Fourth Industrial Revolution, and for this reason, this document presents the development of an AR application for real-time control of a Robotic Arm with IoT communication. The application provides AR visualization of the state of the process in real-time, as well as an interface for controlling the position of the robotic arm. The document presents a detailed description of the hardware architecture that includes the AR device, the AR marker, and the infrastructure used for interacting with the robot. In addition, it is presented the architecture of the software for communication of the AR application with the process, to establish remote and real-time control of the position of the robotic arm using IoT. The AR application has been tested with a robotic arm used in the field of medicine, participating in the analysis process of blood chemistry in biological tests carried out in clinical laboratories. The development of this application has various advantages, highlighting the use of low-cost technological devices, easy acquisition, and the remote connection that improves the control efficiency of the robot, improving the security for user integrity because he/she is not exposed to dangerous environments.
A Study on User Experience of Non-linguistic Communication: Focusing on the Interactive Emojis in On-line Meeting Platforms

The pandemic period spanning over two years has shifted most offline meetings and working processes online. People still say that online communication is less accurate in terms of satisfaction with communication as much as face-to-face communication because many parts of human communication can be clearly communicated through verbal expressions, as well as facial expressions, hand gestures, and gestures. The hypothesis of this research is to increase the satisfaction and accuracy of communication in non-face-to-face communication, it is necessary to study user experience on non-face-to-face communication methods. The aim of this research is to analyze the non- linguistic communication tools in meeting platform, especially on emojis that can be added in real time during the on-line meeting. For this, among the recently used online meeting platforms, four were selected and the user experience of using emoji to help non-verbal communication between participants was analyzed. The participants of user test are mainly 20 s and 30 s who have online education experience or work experience as well as personal on -line real time face-to face chatting in daily lives. This paper carried out FGI user test with closed format of 5 different key stages in every platform. As a result, this paper finds out your hidden needs in non-linguistic communication in on-line meetings. It also followed by comprehensive evaluation of user’s satisfaction analysis. Since these platforms are in rapid upgrading and changes, Platforms are upgrading and evolving too quickly now, but people’s complex and convergence methods for communication are elements that have been together in people’s society for a long time, so ‘cultural human factors’ have common characteristics and needs. Therefore, the results of this study are expected to contribute as basic research to the design and technology development from the user’s point of view better in the future in non-face-to-face communication. Also, from the future perspectives of development of HCI technology, this paper shows further close location of cultural and humane needs.
Automatic Embedding Interventions for the Classification of Hematopoietic Cells

The classification of hematopoietic cells is the most essential step in automating the analysis of human bone marrow samples. However, the complex structure of cell classes as well as class imbalance make this a challenging task, even for neural networks. Based on projective latent interventions, we propose automatic interventions that iteratively update a learned embedding with suitable transformations that shift different cell types apart and contract samples of the same type together. We present different ways of applying these: either directly on a higher-dimensional embedding or in a parametric version in two dimensions. We analyze the hyper-parameters and evaluate the proposed approach on a challenging dataset of hematopoietic cells. The results show an improvement of up to 3 percentage points for the classification F-score.
Klee’s Measure Problem Made Oblivious

Abstract We study Klee’s measure problem — computing the volume of the union of n axis-parallel hyperrectangles in $$\mathbb {R}^d$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mi>d</mml:mi> </mml:msup> </mml:math> — in the oblivious RAM (ORAM) setting. For this, we modify Chan’s algorithm [12] to guarantee memory access patterns and control flow independent of the input; this makes the resulting algorithm applicable to privacy-preserving computation over outsourced data and (secure) multi-party computation. For $$d = 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> , we develop an oblivious version of Chan’s algorithm that runs in expected $$\mathcal {O}(n \log ^{5/3} n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:msup> <mml:mo>log</mml:mo> <mml:mrow> <mml:mn>5</mml:mn> <mml:mo>/</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> time for perfect security or $$\mathcal {O}(n \log ^{3/2} n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:msup> <mml:mo>log</mml:mo> <mml:mrow> <mml:mn>3</mml:mn> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> time for computational security, thus improving over optimal general transformations. For $$d \ge 3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> , we obtain an oblivious version with perfect security while maintaining the $$\mathcal {O}(n^{d/2})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mrow> <mml:mi>d</mml:mi> <mml:mo>/</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> runtime, i. e., without any overhead. Generalizing our approach, we derive a technique to transform divide-and-conquer algorithms that rely on linear-scan processing into oblivious counterparts. As such, our results are of independent interest for geometric divide-and-conquer algorithms that maintain an order over the input. We apply our technique to two such algorithms and obtain efficient oblivious counterparts of algorithms for inversion counting and computing a closest pair in two dimensions.
Crowd Counting During a Pandemic to Find Out Community Response to Activity Restriction Policy Using Deep Learning

The pandemic has occurred globally, especially in Indonesia since March 2019. It has been almost 2 years since the danger of spreading the COVID-19 virus was still lurking. Every effort has been made by the government by providing information, counseling, vaccines, and even regulations that limit the level of crowds to a certain scale according to the level of COVID-19 sufferers for each region. Currently, in early 2022, community activities in the city of Malang are allowed to meet face-to-face, such as in the education or office sector. However, if you look at the streets of the Malang city area, you can still see that people are sometimes careless because of the declining death graph due to COVID19. In fact, the government still urges the public to continue implementing the health protocol in various activities and limits the scale of the crowd on certain days. The researcher observes the public's response to all regulations given by the government by detecting objects and crowd counting at two points in the Malang city area, namely Jalan Soekarno Hatta and the entrance to the Malang toll road. The input in the form of photos of street crowds will be processed using computer vision and deep learning to identify the type of object for later analysis of the calculation results according to the calendar and the level of restrictions on current community activities. The percentage of object detection accuracy using deep learning is 80% using a confidence threshold value of 0.3.
Sentiment Analysis of Board Secretaries’ Q&amp;R Data

In the Internet era, due to the rapid development of investors communication with public companies, people have diversified ways to express their opinions, thus generating a large amount of data, which contains valuable information. In this paper, we use a combination of the financial sentiment dictionary and Bert to analyze the sentiment of investors’ questions based on the Q&R data of board secretaries on the platform "Easy Interactive" (http://irm.cninfo.com.cn/) launched by Shenzhen Stock Exchange, and the final accuracy rate is 92%, which is 16% higher than the traditional sentiment analysis methods. Compared with offline research, financial news, stock forums, social software, and other data, the Q&R data selected in this paper has less noise and is more intuitive. Moreover, this paper considers knowledge in the financial domain in sentiment analysis and has domain friendliness and model generalization in the financial domain by combining the financial domain sentiment lexicon with the Bert model with adversarial training.
On the Variance of Interval Type-2 Fuzzy Sets

This paper presents some theoretical considerations about the variance of a fuzzy set and introduces a covering–based algorithm for computing the bounds of the variance of an interval Type–2 fuzzy set. The proposed algorithm obtains the bounds of all possible variances of an interval Type–2 fuzzy set unlike other algorithms which are based on the center of centroid of an interval Type–2 fuzzy set to obtain a relative variance which is proven to be always bigger than the absolute variance. A comparison to the relative variance of an interval Type–2 fuzzy set is given through different examples where the proposed algorithm shows better precision than the KM algorithm.
Applying categories with prototypes to incomplete observations in artificial cognitive agents

Studies show that artificial cognitive agents can be equipped with mechanisms for learning of basic language categories. However, after a period of initial learning performed under perfect circumstances, agents get to be deployed into dynamic real world environments leading to possibly partial observations of agent's surroundings. This paper presents a general strategy for applying categories with prototypes on incomplete observational data. It is assumed that the task is carried out by an artificial agent which has autonomously developed its private ontological knowledge base using complete observations. The agent expresses its internal uncertainty about an assignment of a category to an observed object by relying on epistemic modal operators of possibility, belief, and knowledge. An underlying theory builds upon accomplishments of a theory of grounding of feasible epistemic statements in artificial cognitive agents.
Perceptual Artifacts Localization for Inpainting

Image inpainting is an essential task for multiple practical applications like object removal and image editing. Deep GAN-based models greatly improve the inpainting performance in structures and textures within the hole, but might also generate unexpected artifacts like broken structures or color blobs. Users perceive these artifacts to judge the effectiveness of inpainting models, and retouch these imperfect areas to inpaint again in a typical retouching workflow. Inspired by this workflow, we propose a new learning task of automatic segmentation of inpainting perceptual artifacts, and apply the model for inpainting model evaluation and iterative refinement. Specifically, we first construct a new inpainting artifacts dataset by manually annotating perceptual artifacts in the results of state-of-the-art inpainting models. Then we train advanced segmentation networks on this dataset to reliably localize inpainting artifacts within inpainted images. Second, we propose a new interpretable evaluation metric called Perceptual Artifact Ratio (PAR), which is the ratio of objectionable inpainted regions to the entire inpainted area. PAR demonstrates a strong correlation with real user preference. Finally, we further apply the generated masks for iterative image inpainting by combining our approach with multiple recent inpainting methods. Extensive experiments demonstrate the consistent decrease of artifact regions and inpainting quality improvement across the different methods. Dataset and code are available at: https://github.com/owenzlz/PAL4Inpaint
The Application and Practice of Data Mining Technology in the Integrated Teaching of Psychology and Computer

With the rapid development of new-generation information technologies such as the mobile Internet and the Internet of things, data has become an important information resource in modern society. The processing of big data is inseparable from the application of data mining (D M G) technology. Therefore, D M G technology is used in all walks of life, plays a very important role. In the education industry, the popularity of the Internet and computers in colleges and universities has extensively affected the psychological concepts of college students. Therefore, it is of great significance to strengthen the integration of psychology and computer teaching and to the development of college students’ mental health. The purpose of this article is to research and analyze the application and practice of D M G technology in the fusion of psychology and computer teaching. This article first summarizes the relevant basic theories of D M G technology, and then extends its core D M G technology. Combining with the current psychology teaching methods in our country’s universities, analyze the existing problems and deficiencies. On this basis, the application practice of D M G technology in the fusion teaching of psychology and computer is used for research and analysis. This article systematically expounds the construction of D M G technology in the teaching system of fusion of psychology and computer, system requirements design and database design. And use questionnaire surveys, field surveys and other research methods to conduct experimental research on the application and practice of D M G technology in the integrated teaching of psychology and computer. Research shows that compared with the traditional face-to-face psychological teaching method, the fusion teaching of psychology and computer based on D M G technology has higher feasibility.
Biosorption of Pb2+ ion by bacterium Pseudomonas sp.

The study aimed to determine the ability of Pseudomonas sp. bacteria to absorb Pb2+ ions based on variations in the concentration of Pb2+ ion exposure to the time (day) required for bacteria in the process of biosorption of Pb2+ ions. Determination of maximum wavelength using a UV-Vis Spectrophotometer with a standard solution of 1.2 ppm Pb2+ ions with a yield of 510 nm. Based on the test results, Pseudomonas sp. bacteria were able to absorb Pb2+ ions at exposure concentrations (4, 8, 12, 16, and 20) ppm with the percentage (%) of biosorption respectively 36,874; 30,548; 20,236; 13,178; and 10,020. The optimum concentration and time required for Pseudomonas sp. bacteria to absorb Pb2+ ions occurred on the 14th day with an exposure concentration of 4 ppm with a biosorption efficiency of 36,388% %.
Inattentive in Social, Active in Mind: VR-Based Design Intervention for Imagining Desirable Possibilities in the Public Space

The metro as a form of public transportation is an important urban infrastructure that takes a large population from place A to B every day. To achieve that, it is primarily designed for extreme functionality and efficiency. However, in terms of experiential aesthetics, the metro is seldom people’s favourite place. When this modern infrastructure succeeds in serving urban mobility with high performance and efficiency, passengers seem to want more than the guaranteed functional performance. Recently, with the emergence of Virtual Reality (VR) technologies, increasing efforts from design and HCI communities look at the value of VR technology in enhancing commuting experiences, bringing new possibilities of interaction and activities, and potentially transforming social public spaces. This study investigates how and why VR technology could be integrated with a metro ride. We experimented with ten passengers by showing them three 360° videos during their metro ride. The results show the narrative-driven scene is most desirable. Despite wearing a VR headset might cause anxiety, our findings indicate a high level of acceptance towards VR experiences based on the finding that it does not challenge the normative behaviours of being a passenger ‘inattentive in social, active in mind’ and further can enhance the experience. As the takeaway, we propose three strategies of VR content tailored for the metro context in which passengers would find a role participating in the virtual scene and turn the scene to one’s own story, and at the same time, maintain physically constrained.
Incorporating Self Attention Mechanism into Polarization spectral image Reflection Network

Reflections caused by glass can affect the performance of computer vision algorithms. Synthesizing reflection pictures is a big part of image reflection removal training. Synthesize images, on the other hand, do not translate well to real-world visuals. In this paper, we propose a novel two-stage deep learning based reflection removal algorithm, which explicitly uses the polarization properties of light. In order to train the network, we collect a large number of real reflected images, including polarization images from different angles. In the first stage, we first predict the initial background image based on neural network. In the second stage, we introduce modified ground truth (MGT) images to reconstruct background images for the purpose of restoring the background. We also introduce a focus module in the network to better extract key features. When an image de-reflection network is trained using MGT images, which are created by mixing the original ground truth (GT) images and the originally de-reflection images, the distortion can be further reduced. The experimental results show that the proposed network can almost completely eliminate the reflection in the image and retain more texture information.
Introduction to Machine Learning

The last two decades have seen a quiet but important revolution in computer science. Now more than ever, computers and algorithms are leading to more prosperous and more accurate insights with software that learns from experience and adapts automatically to match the needs of its tasks [1]. Formerly, the programmer decided how the system would work by manually writing the code. Today, we do not write programs but rather collect data consisting of instruction insights, and develop the algorithms changes that manipulate it as necessary to extract patterns and insights. Today, we have programs that can recognize faces and fingerprints, understand speech, translate, navigate, drive a car, recommend movies, and many more [1]. This is possible now because of artificial intelligence (AI) and its fields, mainly machine learning.
Audio Visual Graph Attention Networks for Event Detection in Sports Video

Interest in event detection for sports video analytics has been increasing because it enables teams to make game plans and can help to reduce the huge working costs of broadcast production. Sports event detection is a challenging task due to it requiring both audio and video features. Several recent methods have modified the transformer model to enable extraction of multi-modal features. These methods input the combined audio and video to the multi-modal transformer and therefore take into account multi-modal interactions. However, the transformer-based method does not focus on significant interactions from the modal interactions, which include ones with emphasis on audio, emphasis on video, or consideration of both audio and video, because it does not pay attention to specific interactions but rather all interactions at once. In this paper, we propose the Audio Visual Graph Attention Networks (AVGAT) model that prepares a different graphical structure for each modal interaction. Consequently, our model can pay attention to the significant modal interactions. Experiments on datasets of actual wheelchair-rugby games showed that AVGAT outperformed the transformer-based model. In addition, an ablation study showed that each component of our model empirically functions well at detecting individual event labels.
Delving into Details: Synopsis-to-Detail Networks for Video Recognition

In this paper, we explore the details in video recognition with the aim to improve the accuracy. It is observed that most failure cases in recent works fall on the mis-classifications among very similar actions (such as high kick vs. side kick) that need a capturing of fine-grained discriminative details. To solve this problem, we propose synopsis-to-detail networks for video action recognition. Firstly, a synopsis network is introduced to predict the top-k likely actions and generate the synopsis (location & scale of details and contextual features). Secondly, according to the synopsis, a detail network is applied to extract the discriminative details in the input and infer the final action prediction. The proposed synopsis-to-detail networks enable us to train models directly from scratch in an end-to-end manner and to investigate various architectures for synopsis/detail recognition. Extensive experiments on benchmark datasets, including Kinetics-400, Mini-Kinetics and Something-Something V1 & V2, show that our method is more effective and efficient than the competitive baselines. Code is available at: https://github.com/liang4sx/S2DNet .
Analysis on the Form of Electronic Music Network Learning Based on Network Information Technology

The development of information network technology provides various forms for subject teaching in colleges and universities. The teaching of music courses in colleges and universities, in the form of information network, can maximize the improvement of teaching quality, but also enable students to master advanced electronic music technology. With the development of information and network technology, there are many forms of music production and communication, which makes music art present a very diverse characteristic. In college music teaching, we should make full use of these information network systems to achieve new teaching methods and better teaching effect.