Reviewerly/exHarmony huggingface dataset:
An Empirical Research on Schema Theory Based Teaching of the Continuation Task for Chinese Senior High School Students

The continuation task, a novel type of source-based writing task which requires students to read and accomplish an unfinished source text, has gained growing popularity in English writing tests, particularly in China. This study employed schema theory in the teaching of senior high school continuation tasks and delved into its resultant effects on learners’ overall writing ability, lexical richness, syntactic complexity, and semantic coherence. Theoretically, it serves as an extension of the Schema theory, Practically, it contributes to the improvement of the continuation task instruction and offers a realistic method to achieving better writing from the perspectives of linguistic schema, content schema, and formal schema. This research revolves around such questions: What effects do schema theory-based Continuation task instructions have on students’ overall writing competence; lexical richness; syntactic complexity and semantic coherence? During the research, two groups of EFL students (N=110) from Chinese senior high schools participated in the trial for fourteen weeks. One group was randomly selected as Control Class where the teacher adopted traditional teaching approaches. At the same time, the other was set up as the Experimental Class guided by schema theory. The participants writing performance were evaluated through the lexical richness, syntactic complexity and semantic coherence. Sample T-test and paired T-test found statistically significant differences between the Control class and the Experimental class. These findings suggest that the schema theory-based approach, such as providing a semantic map or outlining the story to participants, was proven effective in honing the students’ overall writing capability, lexical richness, syntactic complexity, and semantic coherence. Because of the chosen research approach, its efficacy is limited concerning language and discourse.
Smifier: A Smart Contract Verifier for Composite Transactions

Ensuring functional correctness of smart contracts is a pressing security concern to blockchain-based systems.With the development of blockchain application, the trading scenarios and function implementation of smart contracts have become increasing complex, containing several interacted contracts or related functions.However, the existing contracts verifiers for proving functional correctness focus on verifying isolated contract or function but ignore the interactions between them, which makes it difficult to verify correctness of composite transactions, i.e., complex transaction scenarios that invoke multiple contracts or trigger a set of transactions.In this paper, we present SMIFIER, a formal verification tool for smart contracts to prove functional properties in composite transactions.SMIFIER defines a set of specifications for composite transactions and can automatically specify properties in these multiple complex transactions.Based on states extraction and mapping, SMIFIER translates annotated Solidity program into Boogie program and verifies relations between functions and properties for interacted contracts.Our experimental evaluation on 12 real-world projects and 65 properties, demonstrates that SMIFIER is practically effective in ensuring functional correctness of properties in composite transactions.
Embedding Knowledge Graphs with Semantic-Guided Walk

Knowledge graph completion can complete knowledge by predicting missing facts, which is a increasingly hot research topic in knowledge graph construction.Prevalent approaches propose to embed knowledge graphs in a lowdimensional vector space and use these embedding to predict, but they neglect either semantic information or graph structures.We propose a new approach to knowledge graph completion named as ATTWALK, which learns embedding by exploiting both structural and semantic features of a knowledge graph.This is achieved by leveraging a key insight that an entities' embedding is influenced by its multi-hop neighbors', which can be further distinguished by their semantic importance to the entity.ATTWALK orchestrates a two-step workflow by first evaluating neighbors' semantic weights using graph attention networks for each entity, then exploring the entities' local structural features by performing a semantic weight guided walk.We evaluate ATTWALK by conducting extensive experiments, which show that ATTWALK outperforms 12 representative approaches on average across 3 publicly available datasets.
Adaptive Prior-Knowledge-Assisted Function Naming Based on Multi-level Information Explorer

Automatic function naming aims to generate a concise and meaningful name for a function, and has become a popular research area.Function naming models based on deeplearning have made significant progress in recent years.Most of the existing neural models represent a function based on the granularity of token or AST (Abstract Syntax Tree) node.However, generating function names requires more fine-grained knowledge of code, but the representation of tokens or AST nodes is not enough to capture global function semantics.In our work, we propose Apker, a novel Adaptive prior-knowledge-assisted function naming based on multi-level information explorer.The Apker includes three modules: Multi-level Information Explorer (MIE), Adaptive Prior Knowledge Adaptor (APKA) and the Generator.The MIE captures the function semantics from a local and global perspective, motivated by the understanding patterns of humans, who will first understand the meaning of each statement and then comb their logical relations to understand the whole function.The APKA uses the pre-retrieved prior knowledge to assist the model, motivated by our observation that certain name tokens can be extracted directly from certain statements and such probability differs significantly in different types of statements.Finally, the Generator generates function names.The experimental results demonstrate that our approach outperforms the baselines by 5.4% in Precision, 12.7% in Recall, and 7.4% in F1-score.
AESPrompt: Self-supervised Constraints for Automated Essay Scoring with Prompt Tuning

Automated essay scoring(AES) aims to automatically assign scores to essays based on the quality of writing.Previous approaches have made many attempts with pre-trained BERT for essay scoring and achieved the state-of-the-art.However, these approaches mainly rely on the high computation cost and ignore the high similarity between text representations.In this paper, we propose a lightweight prompt-tuning framework, AESPrompt, to capture the significant semantic features of the text efficiently.We construct one continuous prompt for each layer of the frozen language model to help the language model understand the essay scoring task.Specially, we design taskrelated self-supervised constraints to capture discourse structure in terms of coherence and cohesion further to enhance the generalization and discourse awareness of the prompt.Experimental results on the public dataset ASAP illustrate that our approach performs competitively in the full data settings and outperforms in one-shot data settings significantly compared with fine-tuning BERT.
Mapping Modern JVM Language Code to Analysis-friendly Graphs: A Pilot Study with Kotlin

Kotlin is a modern JVM language, gaining adoption rapidly and becoming Android official programming language.With its widely usage, the need for code analysis of Kotlin is increasing.Exposing code semantics explicitly with a properly structured format is the first step in code analysis and the construction of such representation is the foundation for downstream tasks.Recently, graph-based approaches become a promising way for encoding source code semantics.However, current works mainly focus on representation learning with limited interpretability and shallow domain knowledge.The known evolvements of code semantics in new-generation programming languages have been overlooked.How to establish an effective mapping between naturally concise Kotlin source code with graph-based representation needs to be studied by analyzing known language features.In this paper, we propose a first-sight, rule-based mapping method, using composite representation with AST, CFG, DFG, and language features.We evaluate mapping strategies with ablation experiments by simulating a code search solution as a downstream task.Our graph-based method with built-in language features outperforms the text-based way without introducing greater complexity.By addressing the practical barriers to extracting and exposing the hidden semantics from Kotlin source code, our study also helps enlighten source code representations for other modern languages.
ORSM: Online Routing and Scheduling Mechanism for Mix-flows in Data Center Networks

Nowadays, diverse cloud services generate a mix of deadline and non-deadline data flows that constitute the mix-flow environment in data center networks. Deadline flows are mainly generated by user-interactive services and must be completed within deadlines, while non-deadline flows are usually produced by data-parallel services and desire a shorter completion time. To satisfy their performance requirements simultaneously, most existing mix-flow scheduling algorithms are dedicated to balancing the resource contention between these two types of flows. However, routing should also be considered in conjunction with scheduling because inappropriate routing tends to prevent scheduling from playing a valid role. In this paper, we propose ORSM, an online routing and scheduling mechanism for mix-flow transport. We formalize the mix-flow routing and scheduling problem as a mixed integer programming problem. ORSM then solves this problem in a distributed manner by leveraging the intrinsic feedback mechanism to determine the path and transmission rate for each flow. Extensive simulation results show that ORSM can effectively reduce the deadline miss ratio of deadline flows by up to 35.7% as well as the average flow completion time of non-deadline flows by up to 38.6% compared to state-of-art mechanisms.
From Spatial to Spectral Domain, a New Perspective for Detecting Adversarial Examples

Deep neural networks (DNNs) have been closely related to the Pandora’s box from the moment of its birth. Although it achieves a high accuracy significantly in real-world tasks (e.g., object detecting and speech recognition), it still retains fatal vulnerabilities and flaws. Malicious attackers can manipulate DNN model misclassification just by adding tiny perturbations to the original image. These crafted samples are also called adversarial examples. One of the effective defense methods is to detect them before feeding them into the model. In this paper, we delve into the representation of adversarial examples in the original spatial and spectral domains. By qualitative and quantitative analysis, it is confirmed that the high-level representation and high-frequency components of abnormal samples contain richer discriminative information. To further explore the influence mechanism between the two factors, we perform an ablation study and the results show a win-win effect. Utilizing the finding, a detecting method (HLFD) is proposed based on extracting high-level representation and high-frequency components. Compared with other state-of-the-art detection methods, we achieve a better detection performance in most scenarios via a series of experiments conducted on MNIST, CIFAR-10, CIFAR-100, SVHN, and Tiny-ImageNet. In particular, we improve detection rates by a large margin on DeepFool and CW attacks.
Portraits of elementary schools in practicing integrated multiliteracy in learning: A phenomenological study

In society 5.0, everyone is faced with multiliteracy. Ideally, this multiliteracy is learned from elementary school. However, information on the implementation of learning that trains multiliteracy has not been widely studied. This study aims to describe or photograph the implementation of multiliteracy learning carried out by teachers in elementary schools. This research is a phenomenological type of qualitative research. The participants of this study were elementary schoolteachers in Indonesia. Data were collected by interviewing teachers and observing multiliteracy learning in schools. Data analysis was carried out using the Bogdan and Biklen model with the stages of data reduction, data presentation, conclusion drawing and verification. The results show that the teachers’ understanding of multiliteracy was not correct. The implementation of learning that leads to multiliteracy learning has not been carried out; it is still limited to reading and writing activities. &#x0D; &#x0D; Keywords: Multiliterate learning, elementary school, phenomenology.
Emotional Expression and Analysis in Music Performance Based on Edge Computing

The expression of emotion in music performance is the soul of music, and the emotion revealed by the performer during the performance can bring emotional resonance to the audience. The emotions expressed by music such as joy, anger, and sadness are the meaning of music’s existence. Music without emotion will be dead. However, the music itself has no emotion at all; it is just a regular sound, so the emotional reading of music performance is very important. Music performance is an interpretation of music, and it is the most important emotional information and communication medium for human beings. Through the appreciation of the works to express the author’s emotions, the different performance forms of musical instruments, dance, and singing bring emotional resonance to the audience. Edge computing is the core technology and edge node of the Internet of everything in the new era, and it is constantly innovating with the rapid development of computers and the great changes brought about by them. Nowadays, people’s demand for emotional information processing of music performances has also increased. The research attention to music performance and the attention to its application technology have also received unprecedented development, so the requirements for the ability of human and machine interaction are getting higher and higher. With the increasing maturity of multimedia and communication technologies, there is an increasing expectation of using computers to express human thoughts and emotions. By combining the two, Dongfeng’s expression and analysis of music emotions through edge computing have also ushered in new developments. For example, people upload or share music and dance videos to their friends through WeChat, QQ, Douyin, etc., which greatly enriches people’s emotional world. The analysis and judgment of music emotion are the main subject of the joint development of both musicology and psychological research. With the help of computer science technology and artificial intelligence and other tools, the purpose of music emotional research can also be achieved. Particularly with the advancement of science and technology and the vigorous development of computer application technology, people’s needs for emotional expression and analysis of music can now be carried out with the help of computers. However, the amount of data generated is extremely large, and using edge servers for data processing can improve the efficiency of analysis and processing to meet people’s needs.
Next-Generation Database Benchmark for Financial Scenarios

As the major financial entity in China, banks have high performance and security requirements for databases and data service solutions. With the progression of data application services in banking, the data types and business scenarios become more diverse, and it is difficult for users to make optimal choices among a wide diversity of database products and data service solutions. In combination with the data application demands of the financial industry, this study comprehensively analyzes the current status of applications of databases in banking, particularly the status and challenges of database localization in recent years, by using literature research and theoretical analysis. In addition, we systematically investigate the database benchmarks of China and other countries, and further prospect the necessity and importance of constructing next-generation database benchmarks for financial scenarios. We find that current database benchmarks have many deficiencies and face various challenges in dealing with the database testing in financial scenarios owing to the complex business logic, diverse data patterns, and high security requirements. Therefore, to build a next-generation database benchmark that can meet the requirements of financial scenarios, we propose several suggestions to address these challenges, which involve the aspects of workloads, data schemes, metrics, and technical architecture.
Continual Learning Based on Knowledge Distillation and Representation Learning

In recent years, continual learning that is more in line with real-world scenarios has received more attention. In order to solve the catastrophic forgetting problem in continual learning, researchers have put forward various solutions, which are simply summarized into three types: network structure-based methods, rehearsal-based methods and regularization-based methods. Inspired by pseudo-rehearsal and regularization methods, we propose a novel Continual Learning Based on Knowledge Distillation and Representation Learning (KRCL) model, which employs Beta-VAE as a representation learning module to extract a shared representation of learned tasks. In addition, Beta-VAE is also used as a generative model to generate pseudo samples of historical task, and KRCL trains the pseudo samples of the previous tasks together with the data of the current task, and then combines the knowledge distillation process to extract the dark knowledge from the old task model to alleviate the catastrophic forgetting. We compare KRCL with the Finetune, LWF, IRCL and KRCL_real baseline methods on four benchmark datasets. The result shows that the KRCL model achieves state-of-the-art performance in standard continual learning tasks.
Early Detection of Suspicious Behaviors for Safe Residence from Movement Trajectory Data

Early detection of people’s suspicious behaviors can aid in the prevention of crimes and make the community safer. Existing methods that are focused on identifying abnormal behaviors from video surveillance that are based on computer vision, which are more suitable for detecting ongoing behaviors. While criminals intend to avoid abnormal behaviors under surveillance, their suspicious behaviors prior to crimes will be unconsciously reflected in the trajectories. Herein, we characterize several suspicious behaviors from unusual movement patterns, unusual behaviors, and unusual gatherings of people, and analyze their features that are hidden in the trajectory data. Meanwhile, the algorithms for suspicious behavior detection are proposed based on the main features of the corresponding behavior, which employ spatiotemporal clustering, semantic annotation, outlier detection, and other methods. A practical trajectory dataset (i.e., TucityLife) containing more than 1000 suspicious behaviors was collected, and experiments were conducted to verify the effectiveness of the proposed method. The results indicate that the proposed method for suspicious behavior detection has a recall of 93.5% and a precision of 87.6%, demonstrating its excellent performance in identifying the possible offenders and potential target places. The proposed methods are valuable for preventing city crime and supporting the appropriate allocation of police resources.
Students' Effort to Improve Speaking Skill

Abstract The aims of this study was to find out students’ efforts in improving speaking skills at University of PGRI Sumatera Barat. This present study employed a qualitative descriptive design with taken from a questionnaire adapted from the College Students Experience Questionnaire (CSEQ) G.D Kuh (2003) as a research instrument. The data collect by distributing questionnaires using Google Forms. There were 49 students first semester in 2021 studying at the Department of English Education, University of PGRI Sumatera Barat who participated in this study. There are seven activity scales namely, library, classroom learning, speaking experience, arts and music activities, campus facilities, groups and organizations, and personal experiences. The results of the analysis showed that students' efforts to improve their English speaking skills, from twenty-seven indicators of translation, students tend to make twenty-one attempts to improve speaking skills. Most students go to the library to find English speaking resources. Students increase their English vocabulary by reading many source books. Students learn in group discussions in class. Most of the students when they meet at their campus location will discuss to improve their speaking skills, etc. It can be concluded that first semester students use varied efforts to improve their speaking skills in varied activities.Keywords: Speaking Skills, Student EffortAbstrakTujuan dari penelitian ini adalah untuk mengetahui upaya mahasiswa dalam meningkatkan keterampilan berbicara di Universitas PGRI Sumatera Barat. Dalam penelitian ini peneliti menggunakan desain deskriptif kualitatif dengan menggunakan angket yang diadaptasi dari College students Experience questionnaire (CSEQ) G.D Kuh (2003) sebagai instrumen penelitian. Pengumpulan data dengan cara membagikan angket menggunakan Google Form. Ada 49 mahasiswa semester satu tahun 2021 belajar di jurusan Pendidikan Bahasa Inggris, Universitas PGRI Sumatera Barat yang berpartisipasi dalam penelitian ini. Ada tujuh skala aktivitas yaitu, perpustakaan, permbelajaran di kelas, pengalaman berbicara, seni dan aktivitas musik, fasilitas kampus, grup dan organisasi, dan pengalaman pribadi. Hasil analisis menunjukkan bahwa upaya mahasiswa dalam meningkatkan keterampilan berbicara bahasa Inggris bervariasi, dari dua puluh tujuh indikator penjabaran, mahasiswa cenderung melakukan dua puluh satu usaha untuk meningkatkan keterampilan berbicara. Sebagian besar mahasiswa pergi ke perpustakaan untuk menemukan sumber materi berbicara bahasa Inggris. Mahasiswa menambah kosa kata Bahasa Inggris mereka dengan membaca banyak buku sumber. Mahasiswa belajar dalam diskusi kelompok di kelas. Sebagian besar mahasiswa ketika bertemu di lokasi kampus mereka akan berdiskusi untuk meningkatkan kemampuan berbicara mereka, dll. Jadi dapat disimpulkan bahwa mahasiswa semester satu menggunakan berbagai upaya untuk meningkatkan keterampilan berbicara dalam berbagai kegiatan.Kata kunci: Keterampilan Berbicara, Upaya Mahasiswa
Generalized zero-shot learning based on dual latent space reconstruction

Generalized Zero-Shot Learning (GZSL) is characterized as a training process that comprises visual samples from seen classes and semantic samples from seen and unseen classes, followed by a testing process that classifies visual samples from seen and unseen classes. Existing zero-shot learning (ZSL) approaches suffer from domain shift and information loss issues as a result of class differences between visible and unseen classes, as well as uneven image distribution. In this study, a generalized zero-shot learning strategy based on dual latent space reconstruction (DLR-GZSL) is proposed. The method aims to establish a latent space of shared semantic and visual information, uses dual learning to align different modal representations to alleviate the domain shift problem, uses triplet loss to improve intra-class diversity and inter-class separability of the generated samples, and uses information bottleneck to retain as much valid generated feature information as possible to reduce information loss. Experiments on the CUB, SUN, AWA1, and AWA2 datasets reveal that the suggested method has more accurate than previous methods, demonstrating its effectiveness.