Reviewerly/exHarmony huggingface dataset:
Kahoot! As A Digital Quiz in Learning English: Graduate Students’ Perspectives

The use of technology in learning English is very important, it can make the process of learning more effective and easy. Here, the kahoot! is one of the digital quiz in learning English, that used online media very process of learning. The purpose of this study is to know the perspective of graduate students’ of English education study about the uses of kahoot! as a digital quiz in learning English. This study used questioners to collects data. The number of the participants involved in this study were 10 students from private university in Yogyakarta that sitting in the second master program. In addition the methodology in this study is descriptive qualitative. This study found that Kahoot! makes the atmosphere of classroom more interactive and energetic, the participant were more interested and motivated. In addition Kahoot! gives the positive influenced in learning process and make the classroom more interactive fun.&#x0D; &#x0D;
Recognition Method with Deep Contrastive Learning and Improved Transformer for 3D Human Motion Pose

Abstract Three-dimensional (3D) human pose recognition techniques based on spatial data have gained attention. However, existing models and algorithms fail to achieve desired precision. We propose a 3D human motion pose recognition method using deep contrastive learning and an improved Transformer. The improved Transformer removes noise between human motion RGB and depth images, addressing orientation correlation in 3D models. Two-dimensional (2D) pose features are extracted from de-noised RGB images using a kernel generation module in a graph convolutional network (GCN). Depth features are extracted from de-noised depth images. The 2D pose features and depth features are fused using a regression module in the GCN to obtain 3D pose recognition results. The results demonstrate that the proposed method captures RGB and depth images, achieving high recognition accuracy and fast speed. The proposed method demonstrates good accuracy in 3D human motion pose recognition.
Blockchain Technology, Sustainability and Future of Public Input Distribution in Zimbabwe

The aftermath of the agrarian reform in Zimbabwe has been characterized by a drastic decrease in agricultural production, plunging the nation into a state of food insecurity and net food importer. The government has made several efforts to restore the country’s former glory through public inputs distribution particularly targeting maize production. These efforts have culminated in the introduction of Operation Maguta, the Presidential Input Scheme, Pfumvudza and the Command Maize Input Scheme with the sole aim of eliminating increased food import expenditure, introducing import substitution in light of comparative advantages, and improving food production as the country moves towards poverty alleviation among farmers and the country at large. However, since the agrarian reform, all the government’s efforts in public input distribution have been characterized by lack of trust due to red tape and high levels of centralization, manipulation of financial records and lack of mutual trust among the responsible government officials and the citizens. This is threatening the sustainability of public input distribution as well as the effectiveness of government efforts to alleviate poverty among citizens and national food security. The incidents of corruption and mismanagement of public inputs distribution remain high and the perpetrators go unpunished because of the manipulation of the recording system being used. With the increasing call for agriculture 4.0 to meet the increasing demand for food to feed the growing national population, blockchain technology has been heralded as a tool in providing technological sustainable agricultural solutions for handling public input distribution and eradicating corruption. This chapter proffers vital insights that blockchain technology can reduce corruption in public input distribution by making it easier to track all financial government/public processes. The challenges to be addressed in this chapter are supply chain related in the distribution of agricultural inputs in Zimbabwe, where the inputs that are meant to drive agro-based production and be an impetus for development and poverty alleviation are being diverted to the black market, the proceeds of which are privately used.
A colleague named Max: A critical inquiry into affects when an anthropomorphised AI (ro)bot enters the workplace

We offer a critical inquiry into the faltering entry of an anthropomorphised AI (ro)bot, an algorithm without physical or visual form, into the workplace in a media consultancy company. While living a digital life in the virtual world, the ro(bot) was given a human name. We highlight the unexpected consequences the humanisation of an early form of artificial intelligence (AI) has on the affects circulating between people and the new technology and between members of different organisational groups. We argue that anthropomorphising technologies such as AI influences the affective life of organisations and amplifies existing discontent between organisational members, complicating the introduction of the technology. Focusing on human–AI interaction, our analysis reveals a rift between managers who are excited and hopeful about the future capabilities of AI and employees who are frustrated and angry about its present shortcomings. We conclude that collective affects play a central role in contemporary technology-driven organisations in which the role people play in relation to the avalanche of AI technologies is often neglected.
Constructing and meta-evaluating state-aware evaluation metrics for interactive search systems

Abstract Evaluation metrics such as precision, recall and normalized discounted cumulative gain have been widely applied in ad hoc retrieval experiments. They have facilitated the assessment of system performance in various topics over the past decade. However, the effectiveness of such metrics in capturing users’ in-situ search experience, especially in complex search tasks that trigger interactive search sessions, is limited. To address this challenge, it is necessary to adaptively adjust the evaluation strategies of search systems to better respond to users’ changing information needs and evaluation criteria. In this work, we adopt a taxonomy of search task states that a user goes through in different scenarios and moments of search sessions, and perform a meta-evaluation of existing metrics to better understand their effectiveness in measuring user satisfaction. We then built models for predicting task states behind queries based on in-session signals. Furthermore, we constructed and meta-evaluated new state-aware evaluation metrics. Our analysis and experimental evaluation are performed on two datasets collected from a field study and a laboratory study, respectively. Results demonstrate that the effectiveness of individual evaluation metrics varies across task states. Meanwhile, task states can be detected from in-session signals. Our new state-aware evaluation metrics could better reflect in-situ user satisfaction than an extensive list of the widely used measures we analyzed in this work in certain states. Findings of our research can inspire the design and meta-evaluation of user-centered adaptive evaluation metrics, and also shed light on the development of state-aware interactive search systems.
A three-dimensional vision measurement method based on double-line combined structured light

In this paper, a structured light vision measurement method using a scanning laser line and a positioning laser line is proposed. The novel method enables the scanning laser plane to slide along a slide rail while maintaining intersection with the positioning laser plane, eliminating the need to determine the scanning direction and moving step. During the measurement process, the laser plane equations need to be recalibrated for each new position, so a real-time calibration method is given. Initially, the geometric barycenter method is employed to detect the subpixel coordinates of the light stripe intersection point. Subsequently, these coordinates are projected into the camera coordinate system using the initial equations of the positioning laser plane. Finally, leveraging the normal information of the initial equation of the scanning laser plane and the three-dimensional coordinates of the light stripe intersection point, the real-time calibration of the scanning laser plane equations can be accomplished. The proposed method enables the three-dimensional reconstruction of objects, and its accuracy is verified through measurements on gauge blocks. Experimental results demonstrate that this method achieves precise and stable three-dimensional reconstruction of object surface shape.
Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks

Abstract Optical neural networks (ONNs) herald a new era in information and communication technologies and have implemented various intelligent applications. In an ONN, the activation function (AF) is a crucial component determining the network performances and on-chip AF devices are still in development. Here, we first demonstrate on-chip reconfigurable AF devices with phase activation fulfilled by dual-functional graphene/silicon (Gra/Si) heterojunctions. With optical modulation and detection in one device, time delays are shorter, energy consumption is lower, reconfigurability is higher and the device footprint is smaller than other on-chip AF strategies. The experimental modulation voltage (power) of our Gra/Si heterojunction achieves as low as 1 V (0.5 mW), superior to many pure silicon counterparts. In the photodetection aspect, a high responsivity of over 200 mA/W is realized. Special nonlinear functions generated are fed into a complex-valued ONN to challenge handwritten letters and image recognition tasks, showing improved accuracy and potential of high-efficient, all-component-integration on-chip ONN. Our results offer new insights for on-chip ONN devices and pave the way to high-performance integrated optoelectronic computing circuits.
Integrating High‐Level Features for Consistent Palette‐based Multi‐image Recoloring

Abstract Achieving visually consistent colors across multiple images is important when images are used in photo albums, websites, and brochures. Unfortunately, only a handful of methods address multi‐image color consistency compared to one‐to‐one color transfer techniques. Furthermore, existing methods do not incorporate high‐level features that can assist graphic designers in their work. To address these limitations, we introduce a framework that builds upon a previous palette‐based color consistency method and incorporates three high‐level features: white balance, saliency, and color naming. We show how these features overcome the limitations of the prior multi‐consistency workflow and showcase the user‐friendly nature of our framework.
Comparing AWS, Azure, and GCP

The top three providers that dominate the global cloud market are Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Because of the long lists of services they offer, it may appear that each can meet every need when in reality they are not “one size fits all” solutions. Each provider has chosen specific markets to excel in; where one provider may be a good solution for a scenario they may not be for another. This chapter evaluates the strength and weakness of each cloud provider and evaluates which options are best based on user requirements. The chapter also compares the three major cloud providers’ market share and strategy, use cases, networks and network performance, Virtual Private Clouds (VPCs), complexity, pricing, and billing. The trend toward a multicloud strategy is discussed. With this background you can begin to decide which of the three cloud providers to use for services or to pursue certifications and career opportunities.
The Changes in Text Cohesion of Senior High School Students Measured by Coh‐Metrix as a Function of Grade Level

National English Curriculum Standards for General High School sets requirements for text cohesion and text cohesion is an essential component of English writing and English writing evaluation. However, previous studies explored text cohesion mainly from perspectives of college students, horizontal comparison and traditional tools. In this study, a corpus of 300 essays by grade level (senior one and senior three) are analyzed computationally by Coh‐Metrix to examine whether text cohesion improves as grade level rises. During the specific process, this paper aims to identify characteristics and discriminate differences between the text cohesion created by the two grades. The main instrument is Coh‐Metrix—a type of computational tool that offers over 200 measurements to quantify texts. This study mainly employs the measures under the category of referential cohesion, LSA and connectives of it to assess text cohesion. The results demonstrate that as the grade level increases, text cohesion gets better. Specifically, these students produce more noun overlap of adjacent sentences, argument overlap of adjacent sentences, noun overlap of all sentences, argument overlap of all sentences, stem overlap of all sentences, LSA overlap of adjacent sentences, LSA overlap of adjacent paragraphs, LSA overlap of given/new sentences all connectives and additive connectives.
Reversibly selective encryption for medical images based on coupled chaotic maps and steganography

Abstract The security and confidentiality of medical images are of utmost importance due to frequent issues such as leakage, theft, and tampering during transmission and storage, which seriously impact patient privacy. Traditional encryption techniques applied to entire images have proven to be ineffective in guaranteeing timely encryption and preserving the privacy of organ regions separated from the background. In response, this study proposes a specialized and efficient local image encryption algorithm for the medical field. The proposed encryption algorithm focuses on the regions of interest (ROI) within massive medical images. Initially, the Laplacian of Gaussian operator and the outer boundary tracking algorithm are employed to extract the binary image and achieve ROI edge extraction. Subsequently, the image is divided into ROI and ROB (regions outside ROI). The ROI is transformed into a row vector and rearranged using the Lorenz hyperchaotic system. The rearranged sequence is XOR with the random sequence generated by the Henon chaotic map. Next, the encrypted sequence is arranged according to the location of the ROI region and recombined with the unencrypted ROB to obtain the complete encrypted image. Finally, the least significant bit algorithm controlled by the key is used to embed binary image into the encrypted image to ensure lossless decryption of the medical images. Experimental verification demonstrates that the proposed selective encryption algorithm for massive medical images offers relatively ideal security and higher encryption efficiency. This algorithm addresses the privacy concerns and challenges faced in the medical field and contributes to the secure transmission and storage of massive medical images.
Assessment of School Improvement Plan 2013-2015: Basis for the Technical Assistance Plan

This study aimed to determine the level of assessment of School Improvement Plan 2013-2015: basis for the Technical Assistance Plan in the Division of Sagay City, Negros Occidental. The respondents of the study were the 72 school heads, 72 teachers-representatives who were member of the school planning team, 72 parents and 25 LGU representatives in the Division of Sagay City. The validated instruments were distributed and discussed during the Focus Group Discussion to the four group of respondents. The study employed the descriptive research design. The processing of data was done with the use of Statistical Package for Social Sciences (SPSS) Software.The mean was used to determine the level of assessment of the four groups of respondents in the three phases and when they are grouped according to their profile variables. Mann-Whitney U-Test was used to determine whether or not significant difference existed on the level of assessment of school improvement plan in the three phases when the four group of respondents were grouped and compared according to their profile variables. Qualitative questions were classified, grouped and synthesized based on the magnitude of observation for intervention. Results of the study showed that the level of assessment of the school heads revealed a very high rating in the Assess, Plan and Act phases and when taken as a whole. Teachers rated the three phases in a high level. Parents rated the Assess phase in a high level, while Plan and Act phases in a moderate level. Likewise, LGU representatives rates the Assess phase in a very high level, Plan phase as high level and the Act phase as moderate level. When differences were considered school heads, parents and LGU representatives find no significant difference on the level of assessment on school improvement plan. Teachers revealed a significant difference in the Assess phase when grouped and compared according to position title. For the Plan phase, a significant difference found when grouped according to highest educational attainment, position title and average family monthly income. Further, for Act phase, a significant difference existed when grouped according to age. The rest of the responses find no significant difference. Examining deeper the results of the study reveals that it is essential for DepEd Division of Sagay City to package technical assistance plan for the four respondents on various issues contributory to the crafting of School Improvement Plan.
Analysis of the Development Trend of Electric Power Technology

After decades of reform and opening up, China's development structure is constantly upgrading, and the quality of development is also constantly improving. As one of the main sources of clean energy, electric energy plays a vital role in people's production and life. Therefore, how to better serve all aspects of the society with power production technology is the current development must pay attention to the topic. This paper focuses on the analysis of the current situation of electric power production technology and its future development trend. Therefore, under the circumstances that logistics resources are limited and logistics activities are increasingly dependent on the market and industrial structure, Henan Province should scientifically plan and rationally allocate and use logistics resources in implementation of "One Belt, One Road" strategy to achieve the best input and output, improve the overall efficiency and level of logistics; vigorously develop local economy from the perspective of system coordination, realize coordination among logistics enterprises, logistics industry coordination, as well as inter-industry coordination and regional coordination.
Alternating Transfer Functions to Prevent Overfitting in Non-Linear Regression with Neural Networks

In nonlinear regression with machine learning methods, neural networks (NNs) are ideally suited due to their universal approximation property, which states that arbitrary nonlinear functions can thereby be approximated arbitrarily well. Unfortunately, this property also poses the problem that data points with measurement errors can be approximated too well and unknown parameter subspaces in the estimation can deviate far from the actual value (so-called overfitting). Various developed methods aim to reduce overfitting through modifications in several areas of the training. In this work, we pursue the question of how an NN behaves in training with respect to overfitting when linear and nonlinear transfer functions (TF) are alternated in different hidden layers (HL). The presented approach is applied to a generated dataset and contrasted to established methods from the literature, both individually and in combination. Comparable results are obtained, whereby the common use of purely nonlinear transfer functions proves to be not recommended generally.
Robust Novel View Synthesis with Color Transform Module

Abstract The advancements of the Neural Radiance Field (NeRF) and its variants have demonstrated remarkable capabilities in generating photo‐realistic novel views from a small set of input images. While recent works suggest various techniques and model architectures that enhance speed or reconstruction quality, little attention is paid to exploring the RGB color space of input images. In this paper, we propose a universal color transform module that can maximally harness the captured evidence for the neural networks at hand. The color transform module utilizes an encoder‐decoder framework that maps the RGB color space into a new latent space, enhancing the expressiveness of the input domain. We attach the encoder and the decoder at the input and output of a NeRF model of choice, respectively, and jointly optimize them to maintain the cycle consistency of the proposed transform, in addition to minimizing the reconstruction errors in the feature domain. Our comprehensive experiments demonstrate that the learned color space can significantly improve the quality of reconstructions compared to the conventional RGB representation. Its benefits are particularly pronounced in challenging scenarios characterized by low‐light environments and scenes with low‐textured regions. The proposed color transform pushes the boundaries of limitations in the input domain and offers a promising avenue for advancing the reconstruction capabilities of various neural representations. Source code is available at https://github.com/sangminkim-99/ColorTransformModule .
H‐ETC2: Design of a CPU‐GPU Hybrid ETC2 Encoder

Abstract This paper proposes a novel CPU‐GPU hybrid encoding method based on the ETC2 format, commonly used on mobile platforms. Traditional texture compression techniques often face a trade‐off between encoding speed and quality. For a better trade‐off, our approach utilizes both the CPU and GPU. In a pipeline we designed, the CPU encoder identifies problematic pixel blocks during the encoding process, and the GPU encoder re‐encodes them. Additionally, we carefully improve the base CPU and GPU encoders regarding encoding speed and quality. As a result, our encoder minimizes compression artifacts, increases encoding speed, or achieves both of these goals compared to previous high‐quality offline ETC2 encoders.