Reviewerly/exHarmony huggingface dataset:
Sentence-BERT Distinguishes Good and Bad Essays in Cross-prompt Automated Essay Scoring

Automated Essay Scoring (AES) refers to a set of processes that automatically assigns grades to student-written essays with machine learning models. Existing AES models are mostly trained prompt-specifically with supervised learning, which requires the essay prompt to be accessible to the system vendor at the time of model training. However, essay prompts for high-stakes testing should usually be kept confidential before the test date, which demands the model to be cross-promptly trainable with pre-scored essay data already in hands. Document embeddings obtained from pretrained language models such as Sentence-BERT (sbert) are primarily expected to represent the semantic content of the text. We hypothesize SBERT embeddings also contain assessment-relevant elements that are extractable by document embedding decomposition through Principal Component Analysis (PCA) enhanced with Normalized Discounted Cumulative Gain (nDCG) measurement. The identified evaluative elements in the entire embedding space of the source essays are then cross-promptly transferred to the target essays written on different prompts for binary clustering task of dividing high/low-scored groups. The result implies non-finetuned SBERT already contains evaluative elements to distinguish good and bad essays.
Strategi Pembelajaran Dengan Menggunakan Media Pembelajaran Kahoot Pada Mata Kuliah PAK Anak

Currently, learning conditions in the modern era are still often used in the form of providing information and rarely use existing technology, therefore this is no longer effective. The use of strategies in learning is very necessary because it can facilitate each learning process in achieving optimal results. . With a learning strategy, it can make learning activities run as expected by the teacher and achieve optimal results where students can easily follow the learning process. Learning activities carried out inside and outside the classroom require a media. There are various forms of learning media used today, such as audio, visual, motion, and so on. The intensity of using technology in learning is very important, but not just using technology as a learning medium or what they use but how the media is used. Descriptive qualitative research method used by researchers with individual interviews, direct observation and using library research in supporting the acquisition of the final results of the research. The purpose of this study was to determine the use of Kahoot! which is used as one of the choices of learning media in varied learning strategies so that the absorption of material is increased and the situation feels more pleasant so that the results of this study indicate that lecturers can easily provide understanding and students more easily absorb material in learning PAK Anak courses, and provide benefits for a university in implementing innovative digital learning.
THE COMPARISON BETWEEN DIRECT METHOD AND GRAMMAR TRANSLATION METHOD IN IMPROVING STUDENTS' READING COMPREHENSION

The purpose of this research is to find out which method is more effective between the Direct Method and the Grammar Translation Method in improving the reading ability of the students at SMPS Berbudi Kopian. The approach used in this research is quantitative with a quasi-experimental design methodology. The samples are class VIII A students as the Grammar Translation Method class and class VIII B as the Direct Method class. Data were analyzed using the SPSS application. Based on the results of the analysis using SPSS in this study, it was shown that the two methods were slightly improved the students’ reading comprehension at SMPS Berbudi Kopian especially in grade VIII. The results of this study also show that the Grammar Translation Method improves students' reading ability more than using the Direct Method. The results showed that the average post-test in the Direct Method class was 68 and the post-test average in the Grammar Translation Method class was 75. In the table (2-tailed) the independent t-test was more than 0.05 (0.128 &gt; 0.05) it can be concluded that there is no significant difference between student achievement in reading comprehension taught using the Direct Method and Grammar Translation Method. So, the null hypothesis (H0) is accepted.
Dimensionality Selection for Hyperbolic Embeddings using Decomposed Normalized Maximum Likelihood Code-Length

Abstract Graph embedding methods are effective techniques for representing nodes and their relations in a continuous space. Specifically, the hyperbolic space is more effective than the Euclidean space for embedding graphs with tree-like structures. Thus it is critical how to select the best dimensionality for the hyperbolic space in which a graph is embedded. This is because we cannot distinguish nodes well with dimensionality that is considerably low, whereas the embedded relations are affected by irregularities in data with excessively high dimensionality. We consider this problem from the viewpoint of statistical model selection for latent variable models. Thereafter, we propose a novel methodology for dimensionality selection based on the minimum description length principle. We aim to introduce a latent variable modeling of hyperbolic embeddings and apply the decomposed normalized maximum likelihood code-length to latent variable model selection. We empirically demonstrated the effectiveness of our method using both synthetic and real datasets.
Multiview Representation Learning from Crowdsourced Triplet Comparisons

Crowdsourcing has been used to collect data at scale in numerous fields. Triplet similarity comparison is a type of crowdsourcing task, in which crowd workers are asked the question “among three given objects, which two are more similar?”, which is relatively easy for humans to answer. However, the comparison can be sometimes based on multiple views, i.e., different independent attributes such as color and shape. Each view may lead to different results for the same three objects. Although an algorithm was proposed in prior work to produce multiview embeddings, it involves at least two problems: (1) the existing algorithm cannot independently predict multiview embeddings for a new sample, and (2) different people may prefer different views. In this study, we propose an end-to-end inductive deep learning framework to solve the multiview representation learning problem. The results show that our proposed method can obtain multiview embeddings of any object, in which each view corresponds to an independent attribute of the object. We collected two datasets from a crowdsourcing platform to experimentally investigate the performance of our proposed approach compared to conventional baseline methods.
Entanglement in Resonance Fluorescence

Particle entanglement is a fundamental resource upon which are based many quantum technologies. However, the up-to-now best sources of entangled photons rely on parametric down-conversion processes, which are optimal only at certain frequencies, which rarely match the energies of condensed-matter systems that can benefit from entanglement. In this Article, we show a way to circumvent this issue, and we introduce a new source of entangled photons based on resonance fluorescence delivering photon pairs as a superposition of vacuum and the Bell state $|\Phi^-\rangle$. Our proposal relies on the emission from the satellite peaks of a two-level system driven by a strong off-resonant laser, whose intensity controls the frequencies of the entangled photons. Furthermore, the degree of entanglement can be optimized for every pair of frequencies, thus demonstrating a clear advantage over existing technologies. Finally, we illustrate the power of our novel source of entangled single-photon pairs by exciting a system of polaritons and showing that they are left in a maximally entangled steady state.
VLSI Design Based on Block Truncation Coding for Real-Time Color Image Compression for IoT

It has always been a major issue for a hospital to acquire real-time information about a patient in emergency situations. Because of this, this research presents a novel high-compression-ratio and real-time-process image compression very-large-scale integration (VLSI) design for image sensors in the Internet of Things (IoT). The design consists of a YEF transform, color sampling, block truncation coding (BTC), threshold optimization, sub-sampling, prediction, quantization, and Golomb-Rice coding. By using machine learning, different BTC parameters are trained to achieve the optimal solution given the parameters. Two optimal reconstruction values and bitmaps for each 4 × 4 block are achieved. An image is divided into 4 × 4 blocks by BTC for numerical conversion and removing inter-pixel redundancy. The sub-sampling, prediction, and quantization steps are performed to reduce redundant information. Finally, the value with a high probability will be coded using Golomb-Rice coding. The proposed algorithm has a higher compression ratio than traditional BTC-based image compression algorithms. Moreover, this research also proposes a real-time image compression chip design based on low-complexity and pipelined architecture by using TSMC 0.18 μm CMOS technology. The operating frequency of the chip can achieve 100 MHz. The core area and the number of logic gates are 598,880 μm2 and 56.3 K, respectively. In addition, this design achieves 50 frames per second, which is suitable for real-time CMOS image sensor compression.
Resilient Consensus Sustained Collaboratively

The recent growth of blockchain technology has accelerated research on decentralized platforms. Initial blockchain platforms decide on what should be added to the ledger based on Proof-of-Work (PoW) consensus protocol. PoW requires its participants to perform large computations and leads to massive energy wastage. Recent blockchains aim to replace PoW through Proof-of-Stake (PoS) and Malicious Fault-Tolerant (MFT) consensus protocols. However, the safety of the ledger created by these protocols is at the mercy of the long-term safe-keeping of the private keys of participants. As a result, these blockchains face long-range attacks. To ameliorate this situation, we present the design of our novel Power-of-Collaboration (PoC) protocol, which guards existing PoS and MFT blockchains against long-range attacks. We show that PoC can be easily appended to existing blockchains and only marginally degrades their throughputs.
An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition

Differential privacy (DP) is one data protection avenue to safeguard user information used for training deep models by imposing noisy distortion on privacy data. Such a noise perturbation often results in a severe performance degradation in automatic speech recognition (ASR) in order to meet a privacy budget ε. Private aggregation of teacher ensemble (PATE) utilizes ensemble probabilities to improve ASR accuracy when dealing with the noise effects controlled by small values of ε. We extend PATE learning to work with dynamic patterns, namely speech utterances, and perform a first experimental demonstration that it prevents acoustic data leakage in ASR training. We evaluate three end-to-end deep models, including LAS, hybrid CTC/attention, and RNN transducer, on the open-source LibriSpeech and TIMIT corpora. PATE learning-enhanced ASR models outperform the benchmark DP-SGD mechanisms, especially under strict DP budgets, giving relative word error rate reductions between 26.2% and 27.5% for an RNN transducer model evaluated with LibriSpeech. We also introduce a DP-preserving ASR solution for pretraining on public speech corpora.
COMPASS: A creative support system that alerts novelists to the unnoticed missing contents

Writing a story is never easy. Even experienced writers sometimes unintentionally omit information from their writings, and it makes the stories not understandable from others. Complementing such unintentionally omitted information using a computer is helpful in providing writing support. Recently, in the field of story understanding and generation, story completion (SC) was proposed to generate the missing parts of an incomplete story. Although its applicability is limited because it requires that the user have prior knowledge of the missing part of a story, missing position prediction (MPP) can be used to compensate for this problem. MPP aims to predict the position of the missing part, but the prerequisite knowledge that "one sentence is missing" is still required. In this study, we propose Variable Number MPP (VN-MPP), a new MPP task that removes this restriction; that is, the task to predict multiple missing sentences or to judge whether there are no missing sentences in the first place. We also propose two methods for this new MPP task. One solves this task end-to-end, while the other learns the two modules separately. The latter allows the writer more flexibility in using the information by making the intermediate outputs between the modules explicit. Based on the novel task, we developed a creative writing support system, COMPASS. The results of a user experiment involving professional creators who write texts in Japanese confirm the efficacy and utility of the developed system. This study aimed to propose a creation support system and, at the same time, to build a relationship of trust between creators and researchers to lay the groundwork for future research and development of creation support AI.
Identification parade in immersive virtual reality - A technical setup

Virtual Reality (VR) has sparked interest within the forensic community, where it is currently used for training purposes and in variety of forensic scenarios. In combination with efficient and user friendly full body 3-Dimensional (3D) documentation methods, VR visualisations present a viable tool for suspect witness identification. The well-known procedure of placing several persons in a room with a one-way-mirror, along with a witness on the other side of the mirror has practical disadvantages. The primary concern implicates the witness(s) and person(s) of interest coming face-to-face prior to the line-up, combined with finding sufficient persons to include within the line-up. Although image identification using printed paper partially resolved this problem, features such as body stature also marks an issue for the recognition and identification process. To test whether VR provides the technical capabilities to perform an identification parade, a total of 15 subjects were 3D documented using the multi-camera device “Photobox”. From this group, one of the documented persons then interrupted a lecture, where consequently, the students were asked afterwards to identify the same person in VR and paper identification sets. It was found that the participating students were able to identify the “suspect” in both datasets. The results imply that VR technology allow users to identify persons. However, as this is a preliminary study the similarity problem was not analysed in this paper and requires further investigation to demonstrate the robustness of this approach.
Analyzing the Mediating Effect of Organizational Citizenship Behavior on the Relationship between HRM Practices and Intellectual Capital in Educational Settings in Pakistan

Human resource management practices are receiving increasing attention in both public and private institutions. Organizations must implement these practices to promote organizational citizenship behavior among staff. These practices create a culture that values and understands OCB, which is beneficial for the development of intellectual capital in schools. The present study examines the relationship between human resource management practices, organizational citizenship behavior, and intellectual capital in schools, using the resource-based view theory. This is a quantitative study, and data were collected through a closed-ended questionnaire consisting of 66 items. An online Google form was used for data collection due to the COVID-19 pandemic and school closures in targeted districts. 248 out of 410 respondents participated in the study. The data were analyzed using SPSS-22 and the results showed that all three variables (HRMP, OCB, and IC) are positively correlated with each other. However, OCB partially mediates the relationship between HRMP and IC. These findings provide valuable insights for educational managers, school principals, and policymakers to improve the education system in Pakistan.
Research on Knowledge Graph Construction and Q&amp;A Application of Campus Network Operation and Maintenance Service Based on Neo4j

The organization form of the data have a decisive impact on the value of the data, in order to play to the role of the data is bigger, use secondary figure database to store data, building the campus network operations services knowledge map, and USES the knowledge map construction question answering system application, meet the daily campus user demand of campus network operations services between teachers and students.
Customer Sentiment Analysis in Hotel Reviews Through Natural Language Processing Techniques

Customer reviews of products and services play a key role in the customers' decision to buy a product or use a service.Customers' preferences and choices are influenced by the opinions of others online; on blogs or social networks.New customers are faced with many views on the web, but they can't make the right decision.Hence, the need for sentiment analysis is to clarify whether opinions are positive, negative or neutral.This paper suggests using the Aspect-Based Sentiment Analysis approach on reviews extracted from tourism websites such as TripAdvisor and Booking.This approach is based on two main steps namely aspect extraction and sentiment classification related to each aspect.For aspect extraction, an approach based on topic modeling is proposed using the semi-supervised CorEx (Correlation Explanation) method for labeling word sequences into entities.As for sentiment classification, various supervised machine learning techniques are used to associate a sentiment (positive, negative or neutral) to a given aspect expression.Experiments on opinion corpora have shown very encouraging performances.
Data Decomposition for Outlier Detection

Abstract Decomposition for complexity minimization has long been a potent application, yet decomposition for outliers has rarely experimented. This paper presents a decomposition approach to detect local and global patterns of outliers in subgroups. The decomposition of the input data using K-means clustering makes homogeneous subgroups. Consequently, it reduces the complexity of data patterns by isolating possible outliers into the sub-groups. This approach creates clusters of homogeneous data points based on the fitness of purpose. They optimize the outlier patterns in the clusters for subsequent mapping of outlier detectors onto the clusters. This decomposition strategy is found to be effective in reducing the complexity of learning for the detectors without deterioration in the overall detection rate. We experimented with this approach on different benchmark data sets using different benchmark detectors. We observed that our data decomposition approach for outlier detection is superior for identifying localized patterns in the partitions and offers a better generalization.
Accelerating the Wheel Factoring Techniques

The efficiency with which an integer may be factored into its prime factors determines several public key cryptosystems&amp;prime; security in use today. Although there is a quantum-based technique with a polynomial time for integer factoring, on a traditional computer, there is no polynomial time algorithm. We investigate how to enhance the wheel factoring technique in this paper. Current wheel factorization algorithms rely on a very restricted set of prime integers as a base. In this study, we intend to adapt this notion to rely on a greater number of prime integers, resulting in a considerable improvement in the execution time. The experiments on composite numbers n reveal that the proposed algorithm improves on the existing wheel factoring algorithm by about 75%
Thoughts on the Design of Water Supply and Drainage of Infection Building——Taking the Infection Building of Xishan District People's Hospital as an Example

The outbreak of COVID-19 in 2020 has swept the world, bringing a huge impact on society and the economy, and at the same time exposing some inadequacies of public health facilities. Combining some key points and solutions of the water supply and drainage design of the new infection building of the People's Hospital of Xishan District, Wuxi City, this paper provides some references and ideas for the water supply and drainage design of similar buildings in the future.
The vocational education thought based on front-end WEB is integrated into the design system of cultural and creative products

In the development of modern education innovation, the vocational education thought of learning and doing, theory and practice, put forward by Huang Yanpei, has a positive impact on vocational education. In essence, vocational education is to transport more high-quality and high-level application-oriented talents for social construction and development, so vocational skills and moral character should be considered in the same position. In vocational education, we should not only put forward diversified channels and carry out ideological and moral education, but also break through the limitation of traditional teaching ideas and optimize the professional quality of professional teachers and students. Therefore, based on the understanding of vocational education thought, according to modern information technology and digital technology, this paper deeply discusses the design and implementation of digital new cultural and creative platform with Web technology as the core, so as to clarify how to integrate traditional cultural and creative product design into vocational education thought.
Research on the comprehensive Literacy test and evaluation of English course students based on cloud education big data

In the innovation and development of modern education, the Internet, big data, cloud computing and so on have had a profound impact on the implementation of college English education curriculum. Especially after the development trend of economic globalization, college English courses pay more attention to optimizing the comprehensive quality of students in the face of the new talent training requirements put forward by the development of social and economic construction. Through the questionnaire survey, the main problems in the field of education are to study the current content of college students' comprehensive English quality test and evaluation, actively explore the test and evaluation path of students' comprehensive English quality under the background of big data, and clarify the implementation countermeasures of future college English courses according to the development trend of cloud education big data technology. On the basis of understanding the development status of cloud education big data technology, this paper constructs the testing and evaluation system of students' comprehensive quality from various aspects, such as motivation, attitude and quality, according to the requirements of English course students' comprehensive literacy training, and finally puts forward clear education management measures.