Reviewerly/exHarmony huggingface dataset:
Towards Efficient Coarse-grained Dialogue Response Selection

Coarse-grained response selection is a fundamental and essential subsystem for the widely used retrieval-based chatbots, aiming to recall a coarse-grained candidate set from a large-scale dataset. The dense retrieval technique has recently been proven very effective in building such a subsystem. However, dialogue dense retrieval models face two problems in real scenarios: (1) the multi-turn dialogue history is re-computed in each turn, leading to inefficient inference; (2) the index storage of the offline index is enormous, significantly increasing the deployment cost. To address these problems, we propose an efficient coarse-grained response selection subsystem consisting of two novel methods. Specifically, to address the first problem, we propose the H ierarchical D ense R etrieval. It caches rich multi-vector representations of the dialogue history and only encodes the latest user’s utterance, leading to better inference efficiency. Then, to address the second problem, we design the D eep S emantic H ashing to reduce the index storage while effectively saving its recall accuracy notably. Extensive experimental results prove the advantages of the two proposed methods over previous works. Specifically, with the limited performance loss, our proposed coarse-grained response selection model achieves over 5x FLOPs speedup and over 192x storage compression ratio. Moreover, our source codes have been publicly released. 1
Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation

The unprecedented accuracy of convolutional neural networks (CNNs) across a broad range of AI tasks has led to their widespread deployment in mobile and embedded settings. In a pursuit for high-performance and energy-efficient inference, significant research effort has been invested in the design of field-programmable gate array (FPGA)–based CNN accelerators. In this context, single computation engines constitute a popular design approach that enables the deployment of diverse models without the overhead of fabric reconfiguration. Nevertheless, this flexibility often comes with significantly degraded performance on memory-bound layers and resource underutilisation due to the suboptimal mapping of certain layers on the engine’s fixed configuration. In this work, we investigate the implications in terms of CNN engine design for a class of models that introduce a pre-convolution stage to decompress the weights at runtime. We refer to these approaches as on-the-fly . This article presents unzipFPGA, a novel CNN inference system that counteracts the limitations of existing CNN engines. The proposed framework comprises a novel CNN hardware architecture that introduces a weights generator module that enables the on-chip on-the-fly generation of weights, alleviating the negative impact of limited bandwidth on memory-bound layers. We further enhance unzipFPGA with an automated hardware-aware methodology that tailors the weights generation mechanism to the target CNN-device pair, leading to an improved accuracy–performance balance. Finally, we introduce an input selective processing element (PE) design that balances the load between PEs in suboptimally mapped layers. Quantitative evaluation shows that the proposed framework yields hardware designs that achieve an average of 2.57× performance efficiency gain over highly optimised GPU designs for the same power constraints and up to 3.94× higher performance density over a diverse range of state-of-the-art FPGA-based CNN accelerators.
Augmented reality—The way forward in patient education for intracranial aneurysms? A qualitative exploration of views, expectations and preferences of patients suffering from an unruptured intracranial aneurysm regarding augmented reality in patient education

Objectives: The goal of this project is to explore the views, expectations and preferences of patients with an unruptured intracranial aneurysm regarding the use of AR in patient education. Methods: To gain an in-depth understanding of the patients' perspective, a face-to-face interview study was conducted using an interview protocol with a predefined topic list. All interviews were audio-recorded and transcribed verbatim afterwards. Transcripts were analyzed using thematic content analyses. Coding was performed using Atlas.ti software. Results: Seventeen interviews were conducted. The views, expectations and preferences of patients regarding patient education with AR could be subdivided into 15 categories, which could be grouped into 4 general themes: 1) experiences with current patient education, 2) expectations of AR in patient education, 3) opportunities and limitations of AR, and 4) out-of-hospital use of an AR application. Patients' expectations were predominantly positive regarding improving patients' understanding of their medical situation and doctor-patient communication. Discusssion: This study suggests that patients with unruptured intracranial aneurysms are open to receive patient education regarding their disease with AR. Patients expect that AR models can help patients with intra-cranial aneurysms better understand their disease, treatment options and risks. Additionally, patients expect AR could improve doctor-patient communication.
NATIONALLY ACCREDITED JOURNAL MANAGEMENT ASSISTANCE AND TRAINING FOR JOURNAL MANAGERS THROUGHOUT THE CITY OF PALANGKA RAYA

The purpose of this training is to increase the understanding of journal managers regarding journal management, especially with regard to national and international indexing, and improve journal management competence (skills) related to how to manage journals, especially related to national and international indexing. Based on the results of the analysis of the responses of the training participants, it can be concluded that: The scientific journal management training that was carried out was very useful in improving the quality of scientific journal management in the IAIN Palangka Raya area in particular and in the city of Palangka Raya in general. of course Have a direct positive impact on increasing the acceptance of scientific journals. In addition, from the participants' responses during the training and mentoring it was concluded that: Improving the quality of management of scientific journals is a very basic component affecting the quality of published journals
The Students' Motivation and Achievement in Learning English: The Correlational Study

The students have a lot on their plates already, and one of the biggest is the academic activity. Researchers have shown a correlation between intrinsic motivation and academic performance. Students' English proficiency is also influenced by their level of interest and effort in learning the language. The purpose of this research was to determine if and how students at MA Muhammadiyah Darul Arqam Punnia Labumpung, Kab. Pinrang is motivated to learn English based on their academic performance. Students' interest in English and effort in studying the language were found to be significantly related. This was demonstrated by a correlational study on students' motivation and their performance on an English language test. The first piece of good news is that the SPSS 21 program's computed r value is greater than the table at both the 5% and 1% significant level (0.367 0.852 &gt; 0.47). Second, the computation of the contribution between student motivation and achievement in English showed that student motivation (variable X) contributed roughly 72.59% contribution to the achievement (variable Y) of the sample class, while the remaining 25.41% was influenced by other factors.&#x0D; &#x0D; Keywords: Studentsâ€™ Motivation, Learning English, and English Achievement.
Dissemination network of referencing errors: A comparison between the two most cited papers in the world

Lowry's paper (1951, Journal of Biological Chemistry) is the most highly cited paper in the world up to 26th, December 2022.Laemmli's paper (1970, Nature) ranks second.These two papers are selected as our sample to compare respective dissemination networks of referencing errors.There are several types of referencing errors, and we focus on the so-called "double error", that is, the co-occurrence of a wrong volume number of a journal and a wrong beginning page number of a paper.Since the probability of a double error is extremely small, so that whenever double error occurs more than once, we can deduce definitely that there are some authors who copy references from the reference list of other publications.Based on the dissemination networks of the double errors of the above-mentioned two papers, three dissemination routes are confirmed.Route 1: copying references from another paper and citing that paper.Route 2: copying references from another paper without citing that paper.Route 3: Sharing at least one author with another paper which carries the same referencing error.Some suggestions are offered to reduce or eliminate the misconduct of copying references without any checking.
Construction of College Vocal Music Innovation Teaching Mode Based on ARCS Model

Abstract To better realize the construction of college vocal music innovation teaching mode, the author proposes a method based on the ARCS model. From the perspective of ARCS motivation theory, the author first analyzes the four elements of motivation, such as “attention”, “personal”, “confidence”, and “satisfaction”, of the five major learning themes proposed by the vocal music curriculum standards in colleges and universities, it clarifies the advantages and disadvantages of the existing textbooks in motivating students and affirms that the ARCS motivation model can be used to introduce the improved BP neural network evaluation method into the evaluation of college teachers’ innovation and entrepreneurship teaching ability, improve the training of BP neural network, improve the evaluation efficiency and simulate the expert’s thinking, the value of vocal music teaching in colleges and universities is proposed, the teaching design principles in college vocal music based on ARCS motivation model theory are proposed. The experimental results show that: According to the model, the number of neurons in the output layer is determined to be 1, which is the evaluation result of college vocal music innovation teaching quality of college teachers, it can be divided into five grades: fail (0~59), pass (60~69), medium (70~79), good (80~89), excellent (90~100). The number of hidden layers is 8, the number of input neurons is 18, the learning rate is 0.05 〜 0.9, the momentum factor coefficient is 0.85, set the convergence error limit to 0.0001. It proves that the effect of constructing the innovative teaching mode of vocal music in colleges and universities based on the ARCS model is satisfactory.
English Teaching Through Literature-Based Instruction: What do Teachers Experience?

Literature-based instruction is one of the methods that the teacher can used in teaching English to improve student’s four basic skills in English. As a result, this paper aimed to investigate a) to identify the procedures used by teacher in implementing literature-based instruction during distance learning to improve four basic English skills in the classroom at Gandhi Memorial International School, b) to analyze the challenges faced by teacher in implementing literature- based instruction during distance learning at Gandhi Memorial International School. The implementation of Literature-Based Instruction was carried out for Primary International School grade 5 at Gandhi Memorial International School. This study used qualitative research as a research method. The subject of this research was an English teacher in class 5B at Gandhi Memorial International School. Data were collected using an observation checklist and interview guidelines. The result of the data that had been collected from the first question was that the procedure carried out by the teacher is to carry out three stages, namely; pre-activity, whilst activity, and post-activity. Then, the findings for the second research question were that there are three challenges that teachers encountered, namely; time constraints, differentiated instructions, and various assessment rubrics.
Fractal Ledger

Scalability is often considered the "Achilles' heel" of blockchain technology. With a traditional chain-based structure, blocks cannot be generated concurrently, thus limiting the throughput and slowing transaction confirmation. Recently emerged graph-based blockchains generally excel in on-chain performance but are weaker in structural flexibility, which is essential for off-chain or cross-chain scaling. In this paper, we propose Fractal Ledger, a novel blockchain model to scale out the ledger in a fractal manner. Fractal Ledger is comprised of fractal units that synthesize both chain and graph structure. Through conversions and interactions of fractal units, Fractal Ledger can share the advantages of both types of blockchains and offer scalability in structure and storage. We introduce the deduction mechanisms for flexible structure scalability and a three-directional folding scheme of lossless on-chain data compression for storage scalability. Our experimental results reveal the effectiveness of Fractal Ledger in enhancing structure and storage scalability.
AdaptGear

Graph neural networks (GNNs) are powerful tools for exploring and learning from graph structures and features. As such, achieving high-performance execution for GNNs becomes crucially important. Prior works have proposed to explore the sparsity (i.e., low density) in the input graph to accelerate GNNs, which uses the full-graph-level or block-level sparsity format. We show that they fail to balance the sparsity benefit and kernel execution efficiency. In this paper, we propose a novel system, referred to as AdaptGear, that addresses the challenge of optimizing GNNs performance by leveraging kernels tailored to the density characteristics at the subgraph level. Meanwhile, we also propose a method that dynamically chooses the optimal set of kernels for a given input graph. Our evaluation shows that AdaptGear can achieve a significant performance improvement, up to 6.49× (1.87× on average), over the state-of-the-art works on two mainstream NVIDIA GPUs across various datasets.
Multi-scale Memory Residual Network Based Deep Learning Model for Network Traffic Anomaly Detection

Models for detecting network traffic anomalies based on deep learning usually exhibit weak generalization, confined representative capacity, and low real-world adaption. In light of this, a multi-scale memory residual network-based model for identifying network traffic anomalies is proposed. The distribution analysis of the three-dimensional feature space illustrates the efficiency of the network traffic data preprocessing technique. The deep learning algorithm enhances the model's capacity to represent data by coupling multi-scale 1DCNN-LSTM networks. The realization of the residual network is shown using the residual network notion as a foundation. Deep feature extraction accelerates model convergence to detect network traffic anomalies accurately and effectively while preventing gradient disappearance, explosion, over fitting, and network damage. The experimental findings show how the multi-scale 1DCNN-LSTM network can improve the model's representational competence and generalization ability. Performance indicators for the model in this study are also superior to those for other deep learning models.
Analysis of the effectiveness of Internet promotion of educational services on the example of the Department of Marketing and Trade of the Vladivostok State University of Economics and Service

During the period of restrictions and prohibitions imposed as a result of the coronavirus pandemic, the possibility of promoting goods and services on the Internet has become particularly relevant. The use of Internet marketing tools not only allows organizations to successfully interact with the target audience in an online environment, but it is often cheaper than promotion through traditional media channels. The object of the study is Internet marketing, the subject of the study is Internet marketing tools and their effectiveness for the promotion of educational services in higher educational institutions. The purpose of this study is to identify effective ways of Internet promotion of educational services of higher educational institutions. The methodological basis of the research is the results of: monitoring and analysis of domestic and foreign publications on the identified issues; analysis of the report of the analytical organization "Levada Center"; analysis of the effectiveness of Internet promotion of the Department of Marketing and Trade of Vladivostok State University of Economics and Service, including analysis of statistical data of the Facebook advertising cabinet; as well as a survey of consumers of educational services. The article analyzes the effectiveness of an advertising campaign to promote educational services using various Internet marketing tools, identifies the most effective of the tools used. The authors' special contribution to the research of the topic is: differentiation of Internet marketing tools depending on the purpose of promoting educational services, as well as evaluation of the effectiveness of an advertising campaign to promote educational services during the admission campaign. The results of the study can be used by the heads of universities and its structural divisions in order to improve the promotion of educational services, as well as by researchers for further research in this area.
AN AFFORDABLE SMART PRODUCT FOR PERFORMING ALL THE DAILY TASKS BLIND PEOPLE NEED

This article introduces comprehensive smart wearable glasses capable of performing all the basic tasks required by blind and visually impaired people daily.This is to help them in all aspects of life, including education, moving alone, as they can communicate, read, and learn.It encourages them to get into various fields.Hence, it makes their lives much better and easier through various essential functions.These functions are: recognizing Egyptian currencies, detecting the most important and frequently used objects (to simplify their communication, and later can be increased easily based on their needs) around the camera, measuring the safe distance in front of the user to walk safely without obstacles (within 5 meters, so that the user does not need to use a cane) and then notifies him, reading text files, extracting the text from any image, recognizing facial expressions, image captioning, reading Arabic text and convert it into Arabic voice, translating English text into Arabic voice, and detecting motion around the user.Also, this proposed device can be employed anywhere without worrying about charging.Furthermore, it is very affordable to suit poor persons or who live in developing countries.
Standardized Teaching Strategies in English Classroom under the "1+X" Certificate System

The proposal of "1+X" certificate system has an overall and important guiding significance for China's vocational education reform.English vocational skill level examination came into being under the background of deepening vocational education.It emphasizes the achievement-oriented, relying on real scenes to investigate and evaluate students' comprehensive ability to use English to solve workplace tasks in specific professional activities and communication situations."X" intuitively reflects the development trend and market demand of related industries, and it is the vane of talent training, and its significance is self-evident.Based on the interpretation of the connotation of the "1+X" certificate system, this paper analyzes the problems existing in standardized English classroom teaching, and puts forward the standardized English classroom teaching strategies under the "1+X" certificate system: adjusting the talent training plan, optimizing the curriculum, strengthening school-enterprise cooperation and strengthening the construction of "double-qualified" teachers for English majors.It provides new ideas for standardized English classroom teaching in the new form.
Research on Copyright Protection of Digital Publications Based on Blockchain Technology

The development of the Internet has made digital publishing an inevitable trend for the transformation and development of the publishing industry, and with it comes a series of problems: easy access within, easy copying and long time to confirm the rights, high transaction and maintenance costs, which makes the copyright issue always a bottleneck for the development of the digital publishing industry. This paper attempts to explore the application path of copyright deposit, copyright review, copyright maintenance, copyright transaction and copyright supervision from the features of distributed ledger and impenetrability of blockchain technology to promote the high-quality development of China's digital publishing industry.
Application of data mining model in English text writing training system

Abstract The big data era of “data-driven schools, educational analytics and transformation” has arrived and information technology has emerged in education. As the social demand for English writing in universities increases, the research of English writing becomes important, and the use and study of big data and information will only become the new standard for teaching and learning English. This study uses the GBDT model to extract and analyze teaching materials prepared by an English university writing skills course, select materials tailored to students’ characteristics, and produce student test scores. Learning models for predicting, analyzing and analyzing students’ writing scores, information technology can accurately predict students’ scores, which ensures the correct use of information technology in English university academic writing and the impact of GBDT. model for predicting outcomes, which has a positive role and value in student academic writing and teacher training.
The curriculum optimization exercise system of college physical aerobics based on partial differential equation

Abstract To better understand the research on how to improve the exercise of the college body aerobics curriculum, a method based on the equality of the half. By using the conditions in the theory of the difference between the variables to solve the selection problem in the multivariate experiment, it is equivalent to the design of the decision importance. Given the current situation of teaching aerobics in physical exercise at colleges and universities, an optimization of teaching aerobics is created. Through the test report, the relevant information has been identified. At this stage, there is no difference between the two groups in terms of skills and theoretical knowledge, but in self-exercise, the P value is less than 0.05, there is a difference, assuming that the New teaching has an important role in the development of students’ ideas. The goal is to use the best methods of teaching aerobics, solving the conflict between teaching hours and teaching methods of teaching aerobics for physical education in buildings colleges, and universities, and strive to make students get the best results in a limited time, thus making people attach importance to theoretical research and ideas of aerobics training.
Statistical Analysis of Advanced Mathematics Teaching Quality Evaluation Based on Quantile Regression Model

Abstract To understand the quantile regression model, research on a statistical analysis of advanced mathematics teaching quality evaluation was proposed. In the research, based on the test bank and questionnaires, statistical analysis was applied to examine the quality of the test bank and investigate the factors affecting the teaching quality, to understand the students’ scores, find the main factors affecting the teaching quality and use them to analyze the students’ learning status and learning quality. This will help teachers understand and grasp the students’ learning process and improve their teaching ability. And it can provide a theoretical basis for qualitative and quantitative analysis to further improve the high quality.