Reviewerly/exHarmony huggingface dataset:
Construction and Simulation of Financial Risk Prediction Model Based on LSTM

To further improve the prediction ability of enterprises in financial risk, a financial risk prediction model based on LSTM is proposed to improve the accuracy of financial risk prediction. The simulation results show that when the dimension and layers of LSTM network are 128 and 4 respectively, the precision of the financial risk prediction model reaches the best level. Compared with Z-score model and Fisher discriminant analysis method, LSTM model shows good performance in F1 measure, precision, accuracy and recall, reaching 93.91%, 94.63%, 94.06% and 95.12% respectively. The above experimental results verify that the financial risk prediction model based on LSTM has feasibility and certain advantages, and has certain practical reference value for the financial risk prediction of enterprises.
ECI: Effective Channel Interaction for Person Search

Person search, a combination of person detection and person re-identification (re-ID), is one of the research hotspots of intelligent security. Person detection treats all people as one of a kind and focuses on the commonalities of people. In contrast, re-ID treats persons with different identities as different classes, focusing on the uniqueness of each individual. In order to better balance these two conflicting tasks, this paper proposes a person search network with effective channel interaction (ECI) to reduce the impact on model performance. First, the channel attention module ECA is inserted into the residual block of the backbone network ResNet50 using NAE as the baseline. The correlation between each channel and its immediate neighbours is strengthened with the help of 1D convolution. This channel interaction approach allows the model to learn autonomously which features of the input image are more dominant and thus adjust the feature weights appropriately. Next, the accuracy of the model is tested for each of the five cases of 1D convolutional kernel size and compare to the original model without the use of channel attention. Experimental results show that our method achieves the highest accuracy of 91.29% and 92.55% for mAP and top-1, respectively, in the large public dataset CUHK-SYSU.
Multi-Modal Sarcasm Detection with Prompt-Tuning

Sarcasm is a meaningful and effective form of expression which people often use to express sentiments that are contrary to their literal meaning. It is fairly common to encounter such expressions on social media platforms. Comparing with the traditional approach of text sarcasm detection, multi-modal sarcasm detection is proved to be more effective when dealing with information on social networks with various forms of communication. In this work, a prompt-tuning method is proposed for multi-modal sarcasm detection (Pmt-MmSD). Specifically, to model the incongruity of text modalities, we first build a prompt-PLM network. Second, to model the text-image incongruity, an inter-modality attention network (ImAN) is designed based on self-attention mechanism. In addition, we utilize the pre-trained Vision Transformer (ViT) network to process the image modality. Extensive experiments demonstrated the effectiveness of the proposed Pmt-MmSD model for multi-modal sarcasm detection, which significantly outperforms the state-of-the-art results.
Rescripting creativity after automation: situating the simulacrum to interpret the queerness of computational creativity

Purpose The purpose of this paper is to examine how automation in the ever-changing technological landscape is increasing integrated into, and has become a significant presence in, our personal lives. Design/methodology/approach Through post qualitative inquiry, the authors provide a contemplation of automation and its effect on creativity, as a contemporary expression of dis/locations, the simulacrum, performative work and a toxic digital presence in socio-cultural-technical spaces. Findings The authors discuss how we behave, contribute, explore, interact and communicate within and across automated digital platforms, has salience for understanding and questioning the ways that dominant discourses in the contemporary construction and enactment of subjectivity, creativity and agency are being modulated by the machine. Originality/value This paper offers a nuanced consideration of creativity, by considering the way creativity is being performed and situated within the effects of automation and its role in dis/locations, performative work and its potential as a the simulacrum in socio-cultural-technical spaces.
Webometric Rankings of Universities in Developing Countries

This chapter highlights the history and purposes of the University ranking systems. It focuses on the reasons and indicators for ranking, roles, and challenges or criticisms of Webometrics ranking universities in developing countries. The world history of universities brought about the need to gauge their qualities. Unfortunately, the rankers use the same scales, criteria, and indicators to rank all universities leaving universities in developed countries enjoying top positions as they have all the requirements compared to developing countries. This chapter highlights the usage of scales, indicators, and criteria to gauge all universities equally the world over. It assesses the pitfalls inherent to Webometrics Ranking used to gauge the performance of universities in developing countries. The chapter understands the roles of Webometrics ranking, opportunities, challenges, and ways forward to fairness and justice in ranking universities.
360EAVP: A 360-degree Edition-Aware Video Player

In this work, we introduce 360EAVP, an open-source Web browser-based application for streaming and visualization of 360-degree edited videos on head-mounted displays (HMD). The proposed application builds upon the Virtual Reality Tile-Based 360-degree Player (VDTP) by adding new functionalities. Specifically, this paper explains the main features introduced by 360EAVP, which are: 1) operation on HMDs based on real-time user's viewport; 2) dynamic editing via "snap-change" or "fade-rotation" combined with "blinking"; 3) visibility evaluation of user's Field of View with respect to the player's cubic projection (for purposes of tile requests); 4) incorporation of editing timing information into the operation of the ABR algorithm; 5) viewport prediction module based on either linear regression or ridge regression algorithms; and 6) data collection and log module during video playback. The introduced application can be freely used to support research on many topics such as optimization of tile-based 360-degree edited video streaming, psycho-physical experiments, dataset generation, and ABR algorithm development, to name a few.
Modeling Gamer Quality-of-Experience Using a Real Cloud VR Gaming Testbed

Cloud Virtual Reality (VR) gaming offloads computationally-intensive VR games to resourceful data centers. Ensuring good Quality of Experience (QoE) in cloud VR gaming, however, is inherently challenging as VR gamers demand high visual quality, short response time, and low cybersickness. In this paper, we investigate the QoE of cloud VR gaming in multiple steps. First, we build a cloud VR gaming testbed, which allows us to measure various Quality of Service (QoS) metrics. Second, we carry out a user study to understand the effects of diverse factors, including encoding settings, network conditions, and game genres on gamer QoE, quantified by Mean Opinion Score (MOS). Using our user study results, we construct QoE models for cloud VR gaming, which to the best of our knowledge, has not been done in the literature. Last, we apply our QoE models to develop a bitrate allocation algorithm for multiple cloud VR gamers to achieve better overall QoE compared to the bandwidth-fair bitrate allocation.
Design and Implementation of a Web-based Multi-user Virtual Reality System for Virtual Tour of Remote Places

In this paper, we present a novel Web-based multi-user Virtual Reality system for a virtual tour of remote places. The proposed system supports not only 360-degree images but also live 360-degree videos. In addition, a synchronization mechanism is proposed to allow multiple users to participate in a virtual tour at the same time. A subjective experiment demonstrates that the proposed system can provide a high level of spatial presence and involvement to users.
Medical visual question answering with symmetric interaction attention and cross-modal gating

The purpose of medical visual question answering (Med-VQA) is to provide accurate answers to clinical questions related to visual content of medical images. However, previous attempts neglect to take full advantage of the information interaction between medical images and clinical questions, which hinders the further progress of Med-VQA. The above issue requires the efforts to focus on critical information interaction within each modality and relevant information interaction between modalities. In this paper, we utilize the multiple meta-model quantifying model as visual encoder and the GloVe word embedding followed by the LSTM as textual encoder to form our feature extraction module. Then, we design a symmetric interaction attention module to construct dense and deep intra- and inter-modal information interaction on medical images and clinical questions for the Med-VQA task. Specifically, the symmetric interaction attention module consists of multiple symmetric interaction attention blocks that contain two basic units, i.e., self-attention and interaction attention. Technically, self-attention is introduced for intra-modal information interaction, while interaction attention is constructed for inter-modal information interaction. In addition, we develop a multi-modal fusion scheme that leverages the cross-modal gating to effectively fuse multi-modal information and avoid redundant information after sufficient intra- and inter-modal information interaction. Experimental results on the VQA-RAD dataset and PathVQA dataset show that our method outperforms other state-of-the-art Med-VQA models, achieving 74.7% and 48.7% on accuracy, 73.5% and 46.0% on F1-score, respectively.
Lower Bound on the Minimum Distance of Single-Generator Quasi-Twisted Codes

We recall a classic lower bound on the minimum Hamming distance of constacyclic codes over finite fields, analogous to the well-known BCH bound for cyclic codes. This BCH-like bound serves as a foundation for proposing some minimum-distance lower bounds for single-generator quasi-twisted (QT) codes. Associating each QT code with a constacyclic code over an extension field, we obtain the first bound. This is the QT analogue to a result in the literature for quasi-cyclic codes. We point out some weaknesses in this bound and propose a novel bound that takes into account the Chinese remainder theorem approach to QT codes as well as the BCH bound of constacyclic codes. This proposed bound, in contrast to previous bounds in the literature, does not presuppose a specific form of code generator and does not require calculations in any extension field. We illustrate that our bound meets the one in the literature when the code generator adheres to the specific form assumed in that study. Various numerical examples enable us to compare and discuss these bounds.
Maintenance Enhancement of Smart Manufacturing Units In Industry 4.0

The fourth industrial revolution has had a significant impact on the modern world. The fourth industrial revolution is primarily driven by the intelligent edge systems. Industry 4.0 is the integration of Big Data, Cloud Computing, Advanced Robotics, Internet of Things, Digital twin, and several other new technologies to enhance production processes that result in better products in a shorter amount of time at a cheaper cost. Tracking of machine health, maintenance through prediction, and scheduling of production are the three main issues facing any industrial sector. Smart manufacturing can address the aforementioned issues by using data-driven deep learning techniques. The model to effectively address the aforementioned difficulties is developed using the deep convolutional LSTM encoder-decoder architecture. LSTM auto-encoder, a feed-forward neural network whose input signal is equal to its output signal, can be used to predict the machine's speed. To put it another way, an auto encoder is an individual research algorithm that uses input data without a labeled target dataset to extract features. Some performance metrics, such as RMSE, MAE, MSE, and SMAPE, are revealed by the experimental results.
TAS: A Temperature-Aware Scheduling for Heterogeneous Computing

With the development of AI technology, the parameters and calculation overhead of advanced models have increased exponentially, resulting in the existing low-end GPU(Graphic Processing Unit) being unable to meet the computing power required for model operation. In order to speed up the inference speed in edge scenarios, various manufacturers have launched NPU(Neural Processor Unit), a special chip for neural networks, which can improve the overall inference efficiency and reduce energy consumption through a certain loss of precision. However, in the current common edge-side solutions, the problem of CPU+GPU+NPU co-processing is not well considered. At the same time, edge-side devices are more easily affected by the ambient temperature. In this paper, CPU+GPU+NPU is used to jointly process edge-side inference tasks, and we first established a heterogeneous device temperature perception model based on the ambient temperature of the edge device, then proposed a TAS(temperature-aware schedule) algorithm to control the running speed of the heterogeneous device, and then proposed a task scheduling algorithm for the heterogeneous device, namely TASTS(TAS-based task schedule). At the same time, we also use a hungarian matching algorithm to optimize the final result. This paper finally verified several models in real edge environment, found that it can improve the performance by 20-50% compared with conventional methods under temperature constraints.
A survey on semantic communications: technologies, solutions, applications and challenges

Semantic Communications (SC) has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks, beyond the theoretical capacity limit. Despite the extensive research on SC, there is a lack of comprehensive survey on technologies, solutions, applications, and challenges for SC. In this article, the development of SC is first reviewed and its characteristics, architecture, and advantages are summarised. Next, key technologies such as semantic extraction, semantic encoding, and semantic segmentation are discussed and their corresponding solutions in terms of efficiency, robustness, adaptability, and reliability are summarised. Applications of SC to UAV communication, remote image sensing and fusion, intelligent transportation, and healthcare are also presented and their strategies are summarised. Finally, some challenges and future research directions are presented to provide guidance for further research of SC.
Virtual Property and Governance Structures with Blockchain

I combine Thorstein Veblen’s “diagnostic” approach with John R. Commons’ “remedial” approach to analyze virtual property. I focus my analysis on public blockchain based discreet assets. I conclude that the failure to fulfill two of the libertarian promises (namely, decentralized and trustless finance) does not discredit blockchain technology as such. Permissioned blockchain has promising applications. However, virtual property that is based on public blockchain facilitates extraction of value that must be politically and juridically regulated together with empowering citizens.
A Study on the Development of Basic Academic Ability Policy Performance Indicators to Improve Policy Acceptance

The purpose of this study was to develop performance indicators of basic education policy in the direction of increasing the acceptance of policy field experts and stakeholders. In order to develop and justify the basic education policy performance indicators, an expert FGI and a questionnaire survey were conducted. As a result of the study, field experts such as superintendents and teachers, and staff at comprehensive learning clinics and those in charge of related organizations said that flexibility is needed when applying the budget execution rate as an evaluation factor by applying different points in the budget invested in basic education policy by city and province offices of education. In addition to short-term performance, mid- and long-term performance should be evaluated separately in consideration of the period required to improve basic academic ability. In this study, the performance indicators developed for each area of input-process-output-result-sustainability evaluation were justified based on quantitative and qualitative data collected to improve field acceptance. Through this study, it is possible to establish the process and elements necessary to develop basic education policy performance indicators suitable for strengthening the accountability of the policy promoting body and increasing policy effectiveness.
Linguistic Analysis of Church Slavonic Documents: A Mixed-Methods Approach

The study is concerned with Church Slavonic manuscripts and early printed books at the threshold of modernity. It has the methodological aim of applying and evaluating a quantitative and qualitative mixed-methods approach for analyzing pre-modern Slavic textual culture. With respect to the quantitative part of the study, the authors make use of the Handwritten Text Recognition tool Transkribus in order to transcribe manuscripts and early printed books automatically and use these transcriptions for quantitative analysis. Qualitative analysis is conducted using established philological methods and visual inspection of the (digital) sources. The sources selected for this study are an East Slavic manuscript of the Church Slavonic Menology (March volume, first quarter of the 16th century) and the South Slavic Cyrillic Služebnik printed in Venice in 1519.Our study shows that quantitative and qualitative methods, when combined in a mixed-methods approach, complement each other in a way that leads to a deeper understanding of the sources investigated and thus of the linguistic development of Church Slavonic.
Fall Detection

Researchers from all across the world are interested in human fall detection and activity recognition. Fall detection is an exciting topic that may be tackled in several ways. Several alternatives have been suggested in recent years. These applications determine whether a person is walking, squatting, or falling, among other activities. Among these tasks detecting elderly falls is crucial. This is because it is a pretty typical and dangerous occurrence that affects people of all ages, with the elderly having a disproportionately negative impact. This is the motivation behind the development of an aged assistance system that detects falls and notifies the caretaker. One of the key characteristics is that it sends an email notification along with a call to the caretaker once a fall is detected.
A study on the design of growth-based evaluation rubric

The purpose of this study is to explore the case of developing a rubric according to the purpose of growth-oriented evaluation and to obtain implications from it. Recently, research and practice on evaluation innovation is expanding. However, the perception of the evaluation rubric has not changed significantly. In most schools, the rubric is recognized only as a “grading criterion.” However, rubrics should also be recognized as ‘guide maps’ that help students reflect on the process and results of learning. Thus, a rubric that reflects the flow of evaluation innovation, such as process-based evaluation and growth-based evaluation, should be developed. In this study, A school was selected as the study school, and an action research was conducted on a case where teachers developed an evaluation rubric. Teachers of A school recognized the limitations of the existing evaluation rubric and developed a rubric that included all stages of ‘planning, performance, and output’ and a rubric that guaranteed ‘opportunities for re-challenge’. In addition, the rubric that embodies the purpose of the growth-based evaluation was confirmed through co-work with external researcher. Judging from these examples, it is necessary to change the perception of the evaluation rubric as a guide that promotes the learning process of students, moving away from the perception of the evaluation rubric as a simple scoring criterion. In addition, studies that comprehensively analyze teachers
Study of Potential Development of Rainfed Land in Maluka Baulin Swamp Irrigation Area

Abstract Agricultural land in South Borneo has high rice productivity. The supporting factors for rice productivity are irrigation areas, one of which is the Maluka Baulin Swamp Irrigation Area (DIR). It is located in Kurau, Tanah Laut Regency. It has an area of 421 ha (Decree of the Minister PUPR of the Republic Indonesia) now to 286 ha (land function and administrative other villages) with use as agricultural land. It so far has a low average rice production and cropping index of one crop in one year. The purpose of this study was to analyze the availability and demand of water well as the provision of irrigation water based on the water balance for the development of cropping patterns and schedules. This study collects primary and secondary data. Preliminary data are location survey, instantaneous discharge, and questionnaire. Secondary data are climatological data, maps of land types, and data on the discharge Tabanio-Bajuin River. The water requirement is obtained from the calculation of land preparation and consumptive use of water for plants. Water availability is obtained by analyzing rainfall, calculating evapotranspiration, calibrating F.J. Mock Model discharge, and calculating mainstay discharge. The temporary result obtained the water balance is land area is obtained with cropping pattern and schedule that increases to paddy-paddy-palawija, which was previously only paddy-paddy. Then, agricultural production, especially rice and secondary crops, will directly grow.
Developing Conversational Agent Using Deep Learning Techniques

Recent advances in artificial intelligence and natural language processing have been widely used in recent years, and one of the best applications of these technologies is conversational agents.These agents are computer programs that can converse with users in natural languages.Developing conversational agents using artificial intelligence techniques is an exciting prospect in natural language processing.In this study, we built an intelligent conversational agent using deep learning techniques.We used a sequence-to-sequence model with encoder-decoder architecture.This encoder-decoder uses a recurrent neural network with long-short-term memory cells.The encoder was used to understand the user's question, and the decoder was to provide the answer.