Reviewerly/exHarmony huggingface dataset:
Construction of a College Physical Education Teaching Model Using Multiple Intelligences Theory

In recent years, China has comprehensively carried out and implemented the “national fitness campaign.” Sports have been integrated into everyone’s daily life. As a place for cultivating talents, colleges and universities have also begun to reform the physical education teaching mode and cultivate a number of talents with strong competition ability, teaching ability, and adaptability to the society. With the deepening of the reform of physical education teaching mode in colleges and universities, some colleges and universities have introduced the theory of multiple intelligences. By analyzing the concept and content of the theory of multiple intelligences and comparing it with the traditional physical education teaching mode, it fully reflects the advantages of the theory of multiple intelligences in physical education teaching. This paper uses fuzzy mathematics and fuzzy set algorithm to build a teaching model based on the theory of multiple intelligences. Moreover, we study the innovative methods of college physical education teaching, take college physical basketball teaching as the research object, and analyze the students’ sports technology, students’ teaching practice ability, and students’ thinking ability based on the theory of multiple intelligences. Experimental outcomes show that the teaching method guided by the theory of multiple intelligences was designed for the intelligent characteristics of the students in the experimental group, and the teachers and students communicated with each other during the teaching period, which significantly improved the students’ thinking ability.
Meet Your Favorite Character: Open-domain Chatbot Mimicking Fictional Characters with only a Few Utterances

Seungju Han, Beomsu Kim, Jin Yong Yoo, Seokjun Seo, Sangbum Kim, Enkhbayar Erdenee, Buru Chang. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
Resource-Efficient Hardware Implementation of Perspective Transformation Based on Central Projection

Perspective correction of images is an important preprocessing task in computer vision applications, which can resolve distortions caused by shooting angles, etc. This paper proposes a hardware implementation of perspective transformation based on central projection, which is simpler than the homography transformation method. In particular, it does not need to solve complex equations, thus no software assistance is required. The design can be flexibly configured with different degrees of parallelism to meet different speed requirements. Implemented on the Xilinx Zynq-7000 platform, 2893 Look-up Tables (LUTs) are required when the parallelism is one, and it can process a 20 Hz video with a resolution of 640 × 480 in real time. When the parallelism is eight, it can process 157 Hz video and requires 11,223 LUTs. The proposed design can well meet the actual needs.
Leverage the Average: Averaged Sampling in Pre-Silicon Side-Channel Leakage Assessment

Pre-silicon side-channel leakage assessment is a useful tool to identify hardware vulnerabilities at design time, but it requires many high-resolution power traces and increases the power simulation cost of the design. By downsampling and averaging these high-resolution traces, we show that the power simulation cost can be considerably reduced without significant loss of side-channel leakage assessment quality. We introduce a theoretical basis for our claims. Our results demonstrate up to 6.5-fold power-simulation speed improvement on a gate-level side-channel leakage assessment of a RISC-V SoC. Furthermore, we clarify the conditions under which the averaged sampling technique can be successfully used.
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks

Since 2017, the Transformer-based models play critical roles in various downstream Natural Language Processing tasks.However, a common limitation of the attention mechanism utilized in Transformer Encoder is that it cannot automatically capture the information of word order, so explicit position embeddings are generally required to be fed into the target model.In contrast, Transformer Decoder with the causal attention masks is naturally sensitive to the word order.In this work, we focus on improving the position encoding ability of BERT with the causal attention masks.Furthermore, we propose a new pre-trained language model DecBERT and evaluate it on the GLUE benchmark.Experimental results show that (1) the causal attention mask is effective for BERT on the language understanding tasks; (2) our DecBERT model without position embeddings achieve comparable performance on the GLUE benchmark; and (3) our modification accelerates the pre-training process and DecBERT w/ PE achieves better overall performance than the baseline systems when pre-training with the same amount of computational resources.
VU-BERT: A Unified Framework for Visual Dialog

The visual dialog task attempts to train an agent to answer multi-turn questions given an image, which requires the deep understanding of interactions between the image and dialog history. Existing researches tend to employ the modality-specific modules to model the interactions, which might be troublesome to use. To fill in this gap, we propose a unified framework for image-text joint embedding, named VU-BERT, and apply patch projection to obtain vision embedding firstly in visual dialog tasks to simplify the model. The model is trained over two tasks: masked language modeling and next utterance retrieval. These tasks help in learning visual concepts, utterances dependence, and the relationships between these two modalities. Finally, our VU-BERT achieves competitive performance (0.7287 NDCG scores) on VisDial v1.0 Datasets.
Integrating Temporal and Spatial Attention for Video Action Recognition

In recent years, deep convolutional neural networks (DCNN) have been widely used in the field of video action recognition. Attention mechanisms are also increasingly utilized in action recognition tasks. In this paper, we want to combine temporal and spatial attention for better video action recognition. Specifically, we learn a set of sparse attention by computing class response maps for finding the most informative region in a video frame. Each video frame is resampled with this information to form two new frames, one focusing on the most discriminative regions of the image and the other on the complementary regions of the image. After computing sparse attention all the newly generated video frames are rearranged in the order of the original video to form two new videos. These two videos are then fed into a CNN as new inputs to reinforce the learning of discriminative regions in the images (spatial attention). And the CNN we used is a network with a frame selection strategy that allows the network to focus on only some of the frames to complete the classification task (temporal attention). Finally, we combine the three video (original, discriminative, and complementary) classification results to get the final result together. Our experiments on the datasets UCF101 and HMDB51 show that our approach outperforms the best available methods.
Estimation of Intensity Duration Frequency for Ungauged Basin in Lampung Province, Indonesia

Frequency duration intensity (IDF) analysis was conducted to estimate the peak flow rate based on the minimum rainfall data collection station. Rainfall data used is data with high intensity that occurs in a short time from automatic rainfall recording stations. Currently, the availability and distribution of automatic rain recording stations in Lampung Province, Indonesia, are still limited. Therefore, this study aims to use the IDF approach in the ungauged basin area for areas with rainfall data that do not meet the hydrological analysis criteria by interpolating rainfall data from 126 manual rainfall measuring stations in Lampung Province, Indonesia. The research method includes analysis of rainfall intensity using the Mononobe equation at various durations and returns periods. Next, create a rainfall intensity map (isohyet) using ArcGis. Finally, compare the IDF analysis of daily rainfall data at 4 automatic rainfall gauge stations with the estimation results based on the intensity map (isohyet). Based on the results of data analysis, it is known that from the available 126 rainfall climatology stations, there are 113 rainfall climatology stations with complete data for 10 years and 13 rainfall climatology stations with incomplete data for 10 years. In addition, the study results show that 45.24% of the daily rainfall in Lampung province is in the low category, 53.97% is in the medium category, and 0.79% is in the high category. This study indicates that rainfall intensity data from climatological rainfall stations that do not meet the hydrological criteria can be found by interpolating rainfall intensity maps from the nearest rain climatology station that meet the hydrological analysis criteria. The relationship test of the actual rainfall intensity variable at 4 automatic rainfall gauge stations with the rainfall intensity from the map (isohyet) using MAPE showed satisfactory results.
Stage-wise Stylistic Headline Generation: Style Generation and Summarized Content Insertion

A quality headline with a high click-rate should not only summarize the content of an article, but also reflect a style that attracts users. Such demand has drawn rising attention to the task of stylistic headline generation (SHG). An intuitive method is to first generate plain headlines leveraged by document-headline parallel data then transfer them to a target style. However, this inevitably suffers from error propagation. Therefore, to unify the two sub-tasks and explicitly decompose style-relevant attributes and summarize content, we propose an end-to-end stage-wise SHG model containing the style generation component and the content insertion component, where the former generates stylistic-relevant intermediate outputs and the latter receives these outputs then inserts the summarized content. The intermediate outputs are observable, making the style generation easy to control. Our system is comprehensively evaluated by both quantitative and qualitative metrics, and it achieves state-of-the-art results in SHG over three different stylistic datasets.
Peningkatan Kemampuan Menulis Teks Prosedur Berbasis Kearifan Lokal Batik Cianjur dengan Menggunakan Media Powtoon

Artikel ini akan memaparkan peningkatan hasil belajar siswa dalam menulis teks prosedur berbasis kearifan lokal, batik Cianjur, menggunakan media Powtoon. Metode yang digunakan yaitu metode eksperimen dengan bentuk one-group pretest-posttest design. Sampel yang terpilih yaitu kelas XI OTKP 2 SMK Negeri 1 Cipanas. Instrumen yang digunakan ialah lembar tes, wawancara, dan angket. Hasil penelitian menunjukkan bahwa hasil belajar menulis teks prosedur siswa mengalami peningkatan, dilihat dari pemerolehan nilai rata-rata tes awal sebesar 66, sedangkan nilai rata-rata dari tes akhir sebesar 87,83. Nilai signifikasi memperoleh nilai Asymp. Sig.0,000 &lt; 0,05 maka Ha diterima, sehingga dapat disimpulkan bahwa penggunaan media Powtoon dapat meningkatkan kemampuan menulis teks prosedur siswa kelas XI SMK Negeri 1 Cipanas. Selain itu, siswa umumnya mengatakan senang belajar dengan menggunakan Powtoon.Katakunci: menulis; Powtoon; teks prosedurAbstract:This article will describe the improvement of students’ learning outcomes in writing procedural texts, that is local wisdom-based, Batik Cianjur, using Powtoon media. The method used was experimental in the form of a one-group pretest-posttest design. The selected sample was class XI OTKP 2 SMK Negeri 1 Cipanas. The instruments used were tests, interviews, and questionnaires. The results of this study indicate that students' learning outcomes of writing procedural texts increased, seen from the average score of the initial test was 66, while the average value of the final test was 87.83. The significance value obtains the Asymp value. Sig.0,000 &lt; 0.05 meaning Ha was accepted, so it can be concluded that the use of Powtoon can improve the ability to write procedural texts for class XI students of SMK Negeri 1 Cipanas. In addition, students' responses reveal that they enjoyed learning by using PowtoonKeywords: writing; Powtoons; procedure text
Research on Practical Byzantine Fault Tolerant Algorithm Based on Trust Mechanism

&lt;p&gt;The consensus algorithm is one of the core technologies of the blockchain, which determines how the nodes in the blockchain network reach a consensus and enable them to jointly maintain a piece of data. This paper focuses on the Practical Byzantine Fault Tolerant Algorithm (PBFT Algorithm), and proposes a Practical Byzantine Fault Tolerant Algorithm based on Trust Mechanism (TM-PBFT Algorithm) to solve the problems of its low fault-tolerant rate and high communication cost. This algorithm supports voting for trusted nodes before the consensus cycle starts. In order to make nodes actively vote and vote for reliable nodes, this paper models the trust degree of nodes. In addition, the time factor is added to the Shapley value to design a new reward and punishment scheme, which makes the distribution of income of each node more reasonable and further encourages nodes to vote for reliable nodes. As the system runs for a long time, the probability of malicious nodes becoming master nodes decreases. Finally, a comparison experiment between the proposed algorithm and the PBFT algorithm shows that the fault tolerance and throughput of the TM-PBFT algorithm are higher than that of the PBFT algorithm, and the communication bandwidth overhead is lower than that of the PBFT algorithm.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Analysis on Cloud Computing Architectures

Cloud computing is an evolving technique and emerged the entire IT industry in recent years. It transformed the IT landscape and brought the world in the hands of public. One can access the resources available on cloud on pay-as-you-use basis, like applications and infrastructure provided by cloud providers in the form of applications already deployed by cloud providers for use by the cloud users. These services are developed, tested and deployed before they brought into use. Another feature of cloud computing is its massive storage infrastructure made available for database and data supplied by the user. The cloud has several unique architectures and many more are still evolving. The services like SaaS, PaaS and the IaaS are deployed on private, public, community and hybrid clouds. In this paper we discuss the present developments in the cloud computing architecture and also try to present feasible guidance for further processing. Papers published in journals, conferences, white papers are analyzed. We attempt to identify, examine and explain the current trends and development in cloud computing architecture. However, only 13% of the papers examined discussed Others-as-a-Service, while only 26% of the papers reviewed considered issues relating to the major actors involved in cloud computing. This will beneficial to cloud providers, users, and researchers alike
An Improved Secure Public Cloud Auditing Scheme in Edge Computing

Cloud storage plays an important role in the data processing of edge computing. It is very necessary to protect the integrity of these data and the privacy of users. Recently, a cloud auditing scheme which can be used to smart cities has been proposed, which is lightweight and privacy-preserving. Although this scheme has very good performance and is a very valuable work, we find that there is insecurity in it. By giving two kinds of attacks, we prove that a malicious cloud server provider (CSP) can forge auditing proof and can successfully pass the verification of the third-party auditor (TPA) even if the CSP deletes the user’s data. Then, based on this scheme, we propose an improved scheme, which can resist the forgery attack from malicious CSP. Through security analysis, our scheme improves the security compared to the original scheme without reducing the efficiency.
To Find Waldo You Need Contextual Cues: Debiasing Who’s Waldo

We present a debiased dataset for the Personcentric Visual Grounding (PCVG) task first proposed by Cui et al. (2021) in the Who's Waldo dataset.Given an image and a caption, PCVG requires pairing up a person's name mentioned in a caption with a bounding box that points to the person in the image.We find that the original Who's Waldo dataset compiled for this task contains a large number of biased samples that are solvable simply by heuristic methods; for instance, in many cases the first name in the sentence corresponds to the largest bounding box, or the sequence of names in the sentence corresponds to an exact left-to-right order in the image.Naturally, models trained on these biased data lead to over-estimation of performance on the benchmark.To enforce models being correct for the correct reasons, we design automated tools to filter and debias the original dataset by ruling out all examples of insufficient context, such as those with no verb or with a long chain of conjunct names in their captions.Our experiments show that our new subsampled dataset 1 contains less bias with much lowered heuristic performances and widened gaps between heuristic and supervised methods.We also demonstrate the same benchmark model trained on our debiased training set outperforms that trained on the original biased (and larger) training set on our debiased test set.We argue our debiased dataset offers the PCVG task a more practical baseline for reliable benchmarking and future improvements.
An E-mail Classification Algorithm based on Stacking Integrated Learning

&lt;p&gt;The text filtering of traditional anti spam system mainly focuses on keyword matching and text fingerprint analysis, which is difficult to accurately identify and classify spam. Therefore, an integrated learning algorithm based on stackin g is proposed in this paper. Firstly, the algorithm takes the manually marked text data of various categories as samples, uses TF-IDF algorithm to train the word vector space model, then selects linear SVC, xgboost and logistic regression algorithm to structure the base classifier, uses random forest algorithm to structure the meta classifier, and combines the stacking ensemble learning algorithm to structure the classification model. It achieves the function of dividing e-mail into five categories: illegal, advertisement, news, bill and recruitment. From the simulation results, the AUC values of the stacking integrated learning classification algorithm for each category are 0.92, 0.95, 1.00, 0.93 and 0.97 respectively, and the AP values are 0.86, 0.88, 1.00, 0.88 and 0.94 respectively, which realizes the high performance and high precision of text classification.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Sign-problem free quantum stochastic series expansion algorithm on a quantum computer

Abstract A quantum implementation of the Stochastic Series Expansion (SSE) Monte Carlo method is proposed, and is shown to offer significant advantages over classical implementations of SSE. In particular, for problems where classical SSE encounters the sign problem, the cost of implementing a Monte Carlo iteration scales only linearly with system size in quantum SSE, while it may scale exponentially with system size in classical SSE. In cases where classical SSE can be efficiently implemented, quantum SSE still offers an advantage by allowing for more general observables to be measured.
Two-Phase Progressive Deep Transfer Learning for Cervical Cancer Dose Map Prediction

Recently, deep learning has enabled the automation of radiation therapy planning, improving its quality and efficiency. However, such progress comes at the cost of amounts of data. For some low incidence cancers, e.g., cervical cancer, the available data is limited, which could degrade the performance of conventional deep learning models. To alleviate this, in this paper, we resort to transfer learning to accomplish the task of dose prediction on a small amount of cervical cancer data. Considering the same scanning areas of the cervical cancer and the rectum cancer and their shared organs at risk, we are inspired to transfer the knowledge learned from rectum cancer (source domain) to cervical cancer (target domain). Specifically, to narrow the huge gap between the source domain and the target domain, we propose a two-phase transfer strategy. Firstly, we aggregate the data distributions of two domains by linear interpolation, and train an aggregated network to perceive the target domain in advance. Secondly, we transfer the knowledge from the well-trained aggregated network to the target network through an innovatively designed Weighted Feature Transfer Module (WFTM), thus ensuring that the target network can learn more valuable knowledge. Experimental results on 130 rectum cancer patients and 42 cervical cancer patients demonstrate the effectiveness of our method.
Ontology-Based Methodology for Knowledge Acquisition from Groupware

Groupware exist, and they contain expertise knowledge (explicit and tacit) that is primarily for solving problems, and it is collected on-the-job through virtual teams; such knowledge should be harvested. A system to acquire on-the-job knowledge of experts from groupware in view of the enrichment of intelligent agents has become one of the important technologies that is very much in demand in the field of knowledge technology, especially in this era of textual data explosion including due to the ever-increasing remote work culture. Before acquiring new knowledge from sentences in groupware into an existing ontology, it is vital to process the groupware discussions to recognise concepts (especially new ones), as well as to find the appropriate mappings between the said concepts and the destination ontology. There are several mapping procedures in the literature, but these have been formulated on the basis of mapping two or more independent ontologies using concept-similarities and it requires a significant amount of computation. With the goal of lowering computational complexities, identification difficulties, and complications of insertion (hooking) of a concept into an existing ontology, this paper proposes: (1) an ontology-based framework with changeable modules to harvest knowledge from groupware discussions; and (2) a facts enrichment approach (FEA) for the identification of new concepts and the insertion/hooking of new concepts from sentences into an existing ontology. This takes into consideration the notions of equality, similarity, and equivalence of concepts. This unique approach can be implemented on any platform of choice using current or newly constructed modules that can be constantly revised with enhanced sophistication or extensions. In general, textual data is taken and analysed in view of the creation of an ontology that can be utilised to power intelligent agents. The complete architecture of the framework is provided and the evaluation of the results reveal that the proposed methodology performs significantly better compared to the universally recommended thresholds as well as the existing works. Our technique shows a notable high improvement on the F1 score that measures precision and recall. In terms of future work, the study recommends the development of algorithms to fully automate the framework as well as for harvesting tacit knowledge from groupware.