Reviewerly/exHarmony huggingface dataset:
A short text clustering study integrating context-awareness and contrast learning

Traditional text representation techniques will produce high-dimensional and sparse vectors for short texts since they have fewer words, which increases the likelihood of noise interference and lowers the accuracy of short text clustering. An alternative model, CASA-Bert-K, is investigated to overcome the aforementioned issues. First, In order to add contextual features into the text representation, context-awareness self-attention networks are combined with Bert. Secondly, To de-optimize the representation of short texts, a simple and effective data augmentation approach to contrast learning is introduced. The text representation module and the clustering module are jointly trained to optimize both text representation and clustering in order to further improve the clustering accuracy. The experimental findings show the researched model's effectiveness in raising the accuracy rate through the use of three datasets in the tests.
Test of independence model between body ownership and agency in a virtual reality environment

Body ownership refers to the feeling that one’s body belongs to that person. Agency is the sense of being able to control one’s own body parts. Three major hypothetical models describe the relationship between body ownership and agency. In this study, we examined whether one of the three models—the independent model—was accurate in describing the aforementioned relationship. We prepared for two conditions using an immersive virtual reality setting. In one condition, both body ownership and agency were evoked. In the other condition, only agency was evoked. We compared the effects of motion delays between these two conditions. The effect of delay on agency did not depend on ownership intensity, supporting the independence of body ownership and agency. However, the higher the reported body ownership was, the higher the reported agency could be, negating their independence. Holistically, the results are not in favor of the independence model.
Moving target recognition algorithm in complex scenes

Target recognition is the basic purpose of computer vision, it is also one of the key components in the field of artificial intelligence. With the advent of the information age, the popularity of video capture tools, massive video data to human identification has brought great challenges. At this stage, video recognition technology has been widely used in simple scenes such as intelligent transportation field and production quality inspection field. How to realize the target recognition and detection from complex scenes has become a more important and difficult issue. In response to this problem, this paper presents a moving target recognition algorithm in complex scenes. First, an improved optical flow algorithm is proposed to mark the moving target region quickly by time series and spatial pixel changes; Secondly, the sliding window of the target area is detected to match the model of each part of the human body, and the feedback information is modeled by a tree structure. Through experiments, this method can detect faster than detection algorithm based on depth learning while ensuring high accuracy, and can meet the requirements of real-time monitoring.
Research on Data Analysis of College Practice Teaching based on LightGBM Classification Algorithm

Undergraduate practice teaching is a key link to improve the quality of talent training. In recent years, all colleges and universities in China are trying their best to develop the construction of practical teaching for undergraduates. Practical teaching has the characteristics of various data features and complex data structure. In order to conduct classification and classification management for practical teaching, improved integrated feature selection algorithm ISFA and LightGBM algorithm are used in this paper to optimize the selection and classification of data features for practical teaching. Using LightGBM integrated classification model of LoF-LGB model, the potential information of practical teaching management data set is explored more deeply. Firstly, integrated feature selection method is used to screen the features of practice teaching, and then LightGBM algorithm is used to classify and predict the selected feature subset. The basic data and statistical data of nodes are taken as objects for comparison experiment. The results show that the proposed method can achieve good results for different node classification targets.
Utterance-style-dependent Speaker Verification by Utilizing Emotions

Conventional speaker verification methods are vulnerable to spoofing by replay attacks, speech synthesis, and voice conversion. Countermeasures against these spoofed voices are thus required. Here we propose a robust utterance-style-dependent speaker verification method in which the user specifies an emotion to the system in advance. The system accepts speech only when spoken with the specified emotion, embodying two-stage matching between the emotion and the speaker. Evaluation experiments confirmed that the proposed method, which used emotion as the key to verification, significantly reduces the false acceptance rate from that of the conventional method. In addition, if the accuracy of emotion verification can be improved, the equal error rate can also be lowered from those of conventional methods. Therefore, using utterance-style-dependency that is our proposed method achieved a robust personal authentication.
Gaze estimation methods based on deep learning: research status, challenge and future

Gaze estimation has been gaining countless currency in recent years, however, that is still confronted with myriad challenges. In the work, we present a brief overview of gaze-based applications, related methods and several dominant plight. First and foremost, we retrospect about the application on basis of gaze. Additionally, we classify the related method into three types: 3D Geometric model, monocular or binocular estimation and deep learning model. Subsequently, we summarize the main challenge, which is still a tough nut for researches to crack. Ultimately, we discuss about the research status and dilemmas for future researchers.
Distributed Machine Learning through Transceiver Competitive Connectivity of Remote Computing Systems

An architecture is built for sequential data transmission among individual machines participating in a decentralized distributed machine learning of a convolutional neural network for image classification. Simultaneously running multiple replicas of a machine learning task using TCP communication protocol has been successfully implemented. A unification of the final result of each epoch until the end of the training session is performed on each of the participating machines in the training process. A working image classification model is obtained on all machines involved in the process. The results from the experiments performed herein show a 63% improvement in training speed for the fastest configuration with only a 3% drop in accuracy compared to the most accurate one. The study therefore confirms that the use of distributed learning systems can have significant benefits for the AI researchers and practitioners.
Exploring the Synergy between Zero-knowledge Proof and Smart Questioning

The need to send secure messages without having to disclose additional information beyond the content; checking data integrity without having visibility into the data itself; Providing the integrity of various systems through validation that does not require the disclosure of sensitive data leads to the discovery of the potential of Zero Knowledge Proof (ZKP) and the technique of Smart Questioning (SQ) to verify the authenticity of the given statement, which involves asking specific questions.
Extended Reality and Control in the Industry

With the upcoming Extended Reality technology we open a new horizon of technology. In this paper we propose the use of VR and AR technology for a more serious business, the new and improved human-machine interface. Being on the back of another industry, in this case the gaming one, will provide us with free R&D, and much cheaper tools for our goals, all the while, the application of this new interface will extend the capability of human control to a whole new level. Hence, why we call it Extended Control (XC). In this paper we have added some of our ideas for applying Extended Control, as well as an example of already existing XC solution with proven increase in efficiency for its platform. Most of the XR tools are new, and the technology has only now become stable enough to be used in serious cases. Therefore, risk management is also taken into account in this paper.
Automatic Construct of Communication Structures in LINE Group Chat

In recent years, communication has been conducted using various chatting tools. As the first step in analyzing communication itself, we propose a method for automatically constructing communication structures in group chats, particularly on LINE, which is widely used in Japan and other countries. First, the posts in the chat history are combined into units that represent the communication flow. Subsequently, the posts are classified into "conversation posts" and "reaction posts." A graph is constructed based on the content and interval of the posts, in which the posts are the nodes and appropriate connections are made between them. Considering a work-related group chat, we confirmed that the structure of the graph automatically constructed using the proposed method is similar to that of the manually constructed graph.
Dynamic Grasping of Aerial Manipulator Based on Coupling Disturbance Compensation Caused by Manipulator and Load

Compared with the grasping of the manipulator on the fixed base, the dynamic grasping of the aerial manipulator on the UAV floating base is a more challenging task. The strong coupling disturbance caused by the manipulator and the load can seriously affect the position tracking performance of the UAV base, leading to the inability of the manipulator's end-effector to accurately reach the grasping position, resulting in the failure of dynamic grasping. To address the issue, this paper presents a coupling disturbance compensation method that comprehensively considers the motion of the manipulator and the load on the end-effector. It can effectively compensate the strong coupling disturbance caused by the manipulator and the load and greatly improve the position tracking performance of the UAV base. In addition, considering that the aerial manipulator is also affected by lumped disturbances such as various uncertainties and wind disturbances, we propose an end-effector position compensation method based on inverse kinematics, so that the end-effector can reach the target position more accurately during the dynamic grasping process. Finally, three sets of comparative simulation results under two scenarios demonstrate the effectiveness of the proposed method.
Operatic Singing Voice Synthesis Using Diff-SVC

Singing voice synthesis technology is widely used in the entertainment field, and it has attracted attention as a method for reproducing the singing voices of the deceased or patients who have lost their voices. In recent years, research has also been conducted on synthesizing singing voices with more human-like expression. The purpose of this study is to develop a system that can synthesize operatic singing voices from the voice of a user who has no experience in opera singing. In this paper, we propose a method for synthesizing an operatic singing voice using speechreading, which uses Diff-SVC voice quality conversion to convert the voice quality of opera singing voice to that of the user. To confirm the effectiveness of the proposed method using Diff-SVC, we compare it to a method using CycleGAN-VC2-based voice conversion.
Belief and Counterfactuals: A Study in Means-end Philosophy <b> <i>Belief and Counterfactuals: A Study in Means-end Philosophy</i> </b> , by F. Huber, Oxford, Oxford University Press, 2022, x + 198 pp., £56.00 (Hardback), ISBN 9780199976119

"Belief and Counterfactuals: A Study in Means-end Philosophy." History and Philosophy of Logic, ahead-of-print(ahead-of-print), pp. 1–2
Technological Immersion and Delegation to Virtual Agents

Interacting with virtual agents in immersive settings is becoming increasingly common thanks to the proliferation of dedicated media devices, such as consumer-grade virtual and augmented reality headsets. These technologies offer many advantages, e.g., in terms of presence and engagement, and can impact user behavior and attitudes toward virtual agents. Recognizing and understanding these effects is essential, especially in critical contexts involving the delegation of high-stake decisions to virtual agents. This article presents two experiments that explore users’ delegatory behavior toward virtual agents experienced via different media devices that vary in their technological immersion, i.e., a device’s technical capacity to deliver immersive experiences. The experiments’ results suggest that technological immersion is not a significant factor in users’ delegation decisions. Thus, for virtual agents designed to carry out critical tasks, developers may focus on other relevant factors, such as agents’ trustworthiness or performance.
Controlled Emission of Entangled Multiphoton States from Cascaded Quantum Wells

Entangled multiphoton states are a critical resource for the emerging applications of quantum science. Generation of reliable sources of such quantum states of light poses both theoretical and practical challenges, particularly in the small scale. We propose a deterministic source of entangled multiphoton states based on spontaneous emission from a ladder of cascaded quantum potential wells. The special ingredient of this scheme compared with a simple ensemble of emitters is the coupling between the two-level systems, creating sublevels in the energy ladder. This enables, via an indistinguishable multipath evolution, the creation of highly entangled multiphoton photon-number combination states in three modes, namely, tripartite entanglement in the frequency degree of freedom. Due to the intricate energy ladder of our proposed structure, we can apply control and switch the entanglement between two multiphoton modes by measuring the third. We further discuss an application as a qubit-pair source with an error-detection ancilla and comment on possible implementations.
A multilingual, multimodal dataset of aggression and bias: the ComMA dataset

In this paper, we discuss the development of a multilingual dataset annotated with a hierarchical, fine-grained tagset marking different types of aggression and the “context" in which they occur. The context, here, is defined by the conversational thread in which a specific comment occurs and also the “type” of discursive role that the comment is performing with respect to the previous comment(s). The dataset has been developed as part of the ComMA Project and consists of a total of 57,363 annotated comments, 1142 annotated memes, and around 70 h of annotated audio (extracted from videos) in four languages—Meitei, Bangla, Hindi, and Indian English. This data has been collected from various social media platforms such as YouTube, Facebook, Twitter, and Telegram. As is usual on social media websites, a large number of these comments are multilingual, and many are code-mixed with English. This paper gives a detailed description of the tagset developed during the course of this project and elaborates on the process of developing and using a multi-label, fine-grained tagset for marking comments with aggression and bias of various kinds, which includes gender bias, religious intolerance (called communal bias in the tagset), class/caste bias, and ethnic/racial bias. We define and discuss the tags that have been used for marking different discursive roles being performed through the comments, such as attack, defend, and so on. We also present a statistical analysis of the dataset as well as the results of our baseline experiments for developing an automatic aggression identification system using the dataset developed. Based on the results of the baseline experiments, we also argue that our dataset provides diverse and ‘hard’ sets of instances which makes it a good dataset for training and testing new techniques for aggressive and abusive language classification.
Hardware Stories. DIY Practices as More-than-Human Material Activism

In relation to an understanding of the symbiotic nature of living organisms, the project “Hardware Stories” is presented as a counternarrative to modernist hygienist standardization. It is a set of diegetic prototypes of construction elements, with relative manufacturing and maintenance tools, and is showcased through a floor component. This work questions how much the mechanism of production of architectural construction materials, with its quality control mechanisms and the economic scale that its implementation requires, actually prevents any form of alternative approaches. The project proposes to consider DIY practices as a form of mundane resistance that can empower multiple actors as a practice of material activism. Through the description of material sourcing, properties, manufacturing processes and installation, this visual essay aims to explain how these enable to conceive different relationships of cohabitation that are critically framed through the concept of “domestication-as-rewilding”, introduced by anthropologist Anna Tsing.
Value of open research data: A systematic evaluation framework based on multi-stakeholder survey

Stakeholders such as funders, data centers and data curators need specific and detailed evidence to support and justify the value of their existing or proposed open data policies and practices. A questionnaire-based evaluation framework was designed through systematic review and expert interviews, focusing on the scientific, economic, and societal values of open research data. Specifically, scientific value was sub-categorized into 24 statements centering around research process, quantity, quality, efficiency, and impact. Economic value included 6 statements related to cost savings and increased returns, while societal value was divided into 7 statements focused on efficiency and quality. The questionnaire survey was conducted among 219 stakeholders from 22 countries/regions. On average, participants (strongly) agreed that open research data has scientific (83.97%), economic (76.94%), and societal (86.89%) value, all 37 statements of the framework were supported and confirmed. This framework is one of the most comprehensive and systematic tools for measuring data value, which can offer support to diverse stakeholders, especially funders, data centers, and data curators in managing and promoting open data systems.
GPU Job Scheduling based on Deep Reinforcement Learning

The development of GPU and machine learning workloads has been profound; however, resource management and allocation have become difficult problems. The methods used nowadays are not suitable for handling numerous data. Inspired by recent advancements in reinforcement learning, which have shown success in solving real AI problems, this paper explores the use of Policy Gradient, Deep Q-Network, and Double Deep Q-Network approaches to optimize GPU job scheduling. The paper introduces a new GPU job scheduling model called DeepGJS (deep GPU job scheduling), which enables efficient resource management by allocating jobs to servers in an optimized manner. DeepGJS aims to minimize slowdown and completion time during job allocation and scheduling. Initial results and analysis indicate that DeepGJS can adapt to various conditions, converge quickly, and outperform baseline methods such as short job first and packer, as well as the DeepRM [11] model. The observed differences between these methods help determine the most suitable approach for different scenarios.
A Novel Batch Streaming Pipeline for Radar Emitter Classification

In electronic warfare, radar emitter classification plays a crucial role in identifying threats in complex radar signal environments. Traditionally, this has been achieved using heuristic-based methods and handcrafted features. However, these methods struggle to adapt to the complexities of modern combat environments and varying radar signal characteristics. To address these challenges, this paper introduces a novel batch streaming pipeline for radar emitter classification. Our pipeline consists of two key components: radar deinterleaving and radar pattern recognition. We leveraged the DBSCAN algorithm and an RNN encoder, which are relatively light and simple models, considering the limited hardware resource environment of a military weapon system. Although we chose to utilize lightweight machine learning and deep learning models, we designed our pipeline to perform optimally through hyperparameter optimization of each component. We demonstrate the effectiveness of our proposed model and pipeline through experimental validation and analysis. Overall, this paper provides background knowledge on each model, introduces the proposed pipeline, and presents experimental results.
Investigation of Web-Based Eye-Tracking System Performance under Different Lighting Conditions for Neuromarketing

The increasing popularity of neuromarketing has led to the emergence of various measurement methods, such as webcam-based eye-tracking technology. Webcam-based eye-tracking technology is noteworthy not only for its use in laboratories but also for its ability to be applied to participants online in their natural environments through a link. However, the complexity of e-commerce interfaces necessitates high performance in eye-tracking methods. This complexity and the applicability of webcam-based eye-tracking technology in various environments have raised research questions about how its performance changes depending on the type and location of lighting. To answer these questions, experiments were conducted with 30 users in two different experimental environments illuminated by artificial and natural methods, with the lighting from the left, right, and front. Participants were asked to focus on targets located in specially prepared graphics for the experiment. In the heatmaps obtained in the eye-tracking tests, the distance and angular difference between the focal point and the target point were measured using the polar coordinate system. The findings indicate that measurements taken with lighting coming from the center were more efficient in both natural and artificial lighting types and measurements taken under natural lighting performed 24% better than artificial ones. Web camera-based eye-tracking technology is a promising method. However, detailed statistical analyses have demonstrated that for complex interfaces like e-commerce, the position and type of lighting are crucial parameters.