Reviewerly/exHarmony huggingface dataset:
Cross-Project Defect Prediction with Metrics Selection and Balancing Approach

Abstract In software development, defects influence the quality and cost in an undesirable way. Software defect prediction (SDP) is one of the techniques which improves the software quality and testing efficiency by early identification of defects(bug/fault/error). Thus, several experiments have been suggested for defect prediction (DP) techniques. Mainly DP method utilises historical project data for constructing prediction models. SDP performs well within projects until there is an adequate amount of data accessible to train the models. However, if the data are inadequate or limited for the same project, the researchers mainly use Cross-Project Defect Prediction (CPDP). CPDP is a possible alternative option that refers to anticipating defects using prediction models built on historical data from other projects. CPDP is challenging due to its data distribution and domain difference problem. The proposed framework is an effective two-stage approach for CPDP, i.e., model generation and prediction process. In model generation phase, the conglomeration of different pre-processing, including feature selection and class reweights technique, is used to improve the initial data quality. Finally, a fine-tuned efficient bagging and boosting based hybrid ensemble model is developed, which avoids model over -fitting/under-fitting and helps enhance the prediction performance. In the prediction process phase, the generated model predicts the historical data from other projects, which has defects or clean. The framework is evaluated using25 software projects obtained from public repositories. The result analysis shows that the proposed model has achieved a 0.71±0.03 f1-score, which significantly improves the state-of-the-art approaches by 23 % to 60 %.
O PÚBLICO E O PRIVADO EM ARQUIVOS DE LABORATÓRIO

The article presents the results of research carried out in laboratories with the objective of verifying the relationship of the scientist and the preservation of documents.The method of data collection was through interviews with the application of a questionnaire.The results show that scientists have freedom of decision on the documentation generated in laboratories and there is no definition or distinction between what is public or private in the laboratory.The theoretical framework used in the research pointed to this question and served as assumptions for the preparation of the questionnaire.
Heterogeneous Graph Neural Network-Based Software Developer Recommendation

In software maintenance, it is critical for project managers to assign software issues to the appropriate developers. However, finding suitable developers is challenging due to the general sparsity and the long-tail of developer-issue interactions. In this paper, we propose a novel Heterogeneous Graph Neural Network-based method for Developer Recommendation (called HGDR), in which text information embedding and self-supervised learning (SSL) are incorporated. Specifically, to alleviate the sparsity of developer-issue interactions, we unify developer-issue interactions, developer-source code file interactions and issue-source code file relations into a heterogeneous graph, and we embed text descriptions to graph nodes as information supplements. In addition, to mitigate the long-tail influence, e.g., recommendation bias, the proficiency weight suppression link supplementation is proposed to complement the tail developers by adjusting proficiency weights. Finally, to fully utilize rich structural information of heterogeneous graph, we use the joint learning of metapath-guided heterogeneous graph neural network and SSL to learn the embedding representation. Extensive comparison experiments on three real-world datasets show that HGDR outperforms the state-of-the-art methods by 6.02% to 44.27% on recommended metric. The experimental results also demonstrate the efficacy of HGDR in the sparse and long-tail scenario. Our code is available at https://github.com/1qweasdzxc/HGDR .
Evading Encrypted Traffic Classifiers by Transferable Adversarial Traffic

Machine learning algorithms have been widely leveraged in traffic classification tasks to overcome the challenges brought by the enormous encrypted traffic. On the contrary, ML-based classifiers introduce adversarial example attacks, which can fool the classifiers into giving wrong outputs with elaborately designed examples. Some adversarial attacks have been proposed to evaluate and improve the robustness of ML-based traffic classifiers. Unfortunately, it is impractical for these attacks to assume that the adversary can run the target classifiers locally (white-box). Even some GAN-based black-box attacks still require the target classifiers to act as discriminators. We fill the gap by proposing FAT (We use FAT rather than TAT to imporove readability.), a novel black-box adversarial traffic attack framework, which generates the transFerable Adversarial Traffic to evade ML-based encrypted traffic classifiers. The key novelty of FAT is two-fold: i) FAT does not assume that the adversary can obtain the target classifier. Specifically, FAT builds proxy classifiers to mimic the target classifiers and generates transferable adversarial traffic to misclassify the target classifiers. ii) FAT makes adversarial traffic attacks more practical by translating adversarial features into traffic. We use two datasets, CICIDS-2017 and MTA, to evaluate the effectiveness of FAT against seven common ML-based classifiers. The experimental results show that FAT achieves an average evasion detection rate (EDR) of 86.7%, which is higher than the state-of-the-art black-box attack by 34.4%.
Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning

Given a model well-trained with a large-scale base dataset, Few-Shot Class-Incremental Learning (FSCIL) aims at incrementally learning novel classes from a few labeled samples by avoiding overfitting, without catastrophically forgetting all encountered classes previously. Currently, semi-supervised learning technique that harnesses freely-available unlabeled data to compensate for limited labeled data can boost the performance in numerous vision tasks, which heuristically can be applied to tackle issues in FSCIL, i.e., the Semi-supervised FSCIL (Semi-FSCIL). So far, very limited work focuses on the Semi-FSCIL task, leaving the adaptability issue of semi-supervised learning to the FSCIL task unresolved. In this paper, we focus on this adaptability issue and present a simple yet efficient Semi-FSCIL framework named Uncertainty-aware Distillation with Class-Equilibrium (UaD-CE), encompassing two modules UaD and CE. Specifically, when incorporating unlabeled data into each incremental session, we introduce the CE module that employs a class-balanced self-training to avoid the gradual dominance of easy-to-classified classes on pseudo-label generation. To distill reliable knowledge from the reference model, we further implement the UaD module that combines uncertainty-guided knowledge refinement with adaptive distillation. Comprehensive experiments on three benchmark datasets demonstrate that our method can boost the adaptability of unlabeled data with the semi-supervised learning technique in FSCIL tasks.
Read the Signs: Towards Invariance to Gradient Descent's Hyperparameter Initialization

We propose ActiveLR, an optimization meta algorithm that localizes the learning rate, $\alpha$, and adapts them at each epoch according to whether the gradient at each epoch changes sign or not. This sign-conscious algorithm is aware of whether from the previous step to the current one the update of each parameter has been too large or too small and adjusts the $\alpha$ accordingly. We implement the Active version (ours) of widely used and recently published gradient descent optimizers, namely SGD with momentum, AdamW, RAdam, and AdaBelief. Our experiments on ImageNet, CIFAR-10, WikiText-103, WikiText-2, and PASCAL VOC using different model architectures, such as ResNet and Transformers, show an increase in generalizability and training set fit, and decrease in training time for the Active variants of the tested optimizers. The results also show robustness of the Active variant of these optimizers to different values of the initial learning rate. Furthermore, the detrimental effects of using large mini-batch sizes are mitigated. ActiveLR, thus, alleviates the need for hyper-parameter search for two of the most commonly tuned hyper-parameters that require heavy time and computational costs to pick. We encourage AI researchers and practitioners to use the Active variant of their optimizer of choice for faster training, better generalizability, and reducing carbon footprint of training deep neural networks.
Características da dor no joelho em idosos usuários da atenção primária à saúde, segundo aspectos sociodemográficos

Knee pain is a condition recognized for its high functional impact in the older population. Knowledge about the characteristics of these demands in Primary Health Care (PHC) is scarce, which makes it difficult to plan lines of care aimed at pain management in this population. The objective of this study was to investigate characteristics of knee pain among older adults users of primary health care, according to sociodemographic aspects. A cross-sectional study was carried out with 201 older adults in three randomly selected health units. Knee pain was classified as chronic (&gt;3 months) and acute (&lt;3 months). The Analog Pain Scale was applied to assess the intensity, scored from 0 to 10, and questions about the impact of pain on daily life and demand for health services. Gender, age, presence of a partner, and education were recorded. Chronic knee pain was reported by 39.8% of participants; 24.9% reported functional impact and 23.9% sought a health service. Presence of pain (p=0.021) and functional impact (p=0.016) were more frequent among those who did not have a partner and the search for health services was more frequent among those with higher education. Acute knee pain was reported by 37.8% of participants, mean pain intensity was 2.4 (SD:3.6), and was associated with higher education and not having a partner. The demand for care for older adults with knee pain in PHC is high and is characterized as being functionally impactful and stimulating the demand for health services, in addition to being unequal in terms of education and the presence of a partner.
Aligning Pakistani Matric English Test with the CEFR

The present research study intends to address crucial issues of English language testing in the Pakistani Matric system in order to contribute towards a more valid, reliable and improved language testing system for the relevant test users, English curriculum designers and developers, researchers and teachers.The focus of the study is to determine how the testing system is comparable in terms of its English language testing procedures to Common European Framework of Reference, a framework for standardized good practice of language testing.The paper analyses the testing systems for secondary level education in Pakistan, Matric and provides a preliminary study to link Matric English test to CEFR.
The Effect of Quantum Learning Model With Big Book Media on Reading and Writing Skills

Reading and writing are very important skills in education. When compared to other countries, the ability to read and write Indonesian people is still low. This study aims to analyze whether the application of the Quantum Learning model with Big Book media can overcome the lack of reading and writing skills. The research design used was a quasi-experimental design. Where the two elementary schools were divided into an experimental class and a control class. Respondents in this study consisted of teachers and second grade students. Data collection techniques used are observation, tests, and documentation. The data analysis techniques used the normality test, homogeneity test, T test, and ANOVA test. The results showed that there was a significant increase in the posttest results of students' reading and writing skills after applying learning with the Quantum Learning model and Big Book media. The results of the ANOVA test showed that learning in the experimental class and control class could improve students' reading and writing skills, although the average value of the increase in the experimental class that applied the Quantum Learning model with Big Book media was higher.
E-Learning Teaching System in Augmentative and Alternative Communication Using Big Data Analytics

The key barriers to children with special needs to contact individuals are to sustain them alertly and efficiently for learning languages. As the first step for students with disabilities is language development. The report contributes to the sector by identifying the obstacles and opportunities. The research proposed an English language teaching in augmentative and alternative communication (ELT-AAC) and was performed using a qualitative approach by extracting evidence from several independent schools with particular students. The results showed the educators were faced with great difficulty in educating advanced students on the language: limited comprehension, classroom equipment, unawareness, and inappropriate language instruction. The research further showed that the problems were mitigated and language instruction improved by special education, another well-designed class, daily psychological counseling, and various viewpoints.
A Study on the Application of Project Learning in Language Subjects

Because project-based learning is contextual, practical and comprehensive, it has a strong relevance to the literacy-based language subject teaching reform. Project-based learning in language subjects is context-based, points to deep learning and has a tendency of holistic learning. It is a shift from designing problems to designing learning, from teaching knowledge to providing scaffolding, and from test orientation to process evaluation. The design of project-based learning in language subjects needs to follow the principles of reverse design, self-construction and goal congruence.
What AI can do for you: for researchers who plan to apply it to their study

This paper surveys the artificial intelligence methodologies which can be applied to electromagnetics and mechanics. This paper describes what AI and machine learning can do for engineers in these fields. It also presents the principles, recent advances and future outlooks of AI applications. As examples, anomaly detection, dentification of material properties and machine characteristics are discussed. Moreover, key points to make effective use of AI for research and development of electric and mechanical systems are described.
Rasch Model Study on Mathematics Examination Test Using Item Response Theory Approach

This study was conducted to analyze the test instrument used to measure the ability of students in the odd final exam in mathematics. Sampling using purposive sampling technique. These students consist of 398 students. The questions given are in the form of multiple-choice questions with a total of 40 items. The data analysis technique used quantitative descriptive analysis. Data analysis was carried out using the Item Response Theory (IRT) Rasch model approach with the help of QUEST software. The results of the analysis show, from 40 items there are 39 items fit with the Rasch model. Judging from the level of difficulty, items with very difficult categories are 0%, difficult categories are 8 items or 21%, moderate item categories are 23 items or 59%, easy categories are 8 items or 21%, and very easy categories are 0 %. The reliability of the estimatedvalue of the item is 0.95 with a very good category so that it affects the items that fit the model. Thehigher reliability, the more items that fit the model. The reliability of the case estimate value is 0.00with a weak category. This value indicates an inconsistency in the answers of the test takers, whichmeans that the test takers are careless in answering the questions, thus affecting the reliability ofthe questions.
Computing the endomorphism ring of an elliptic curve over a number field

We describe deterministic and probabilistic algorithms to determine whether or not a given monic irreducible polynomial H in Z[X] is a Hilbert class polynomial, and if so, which one. These algorithms can be used to determine whether a given algebraic integer is the j-invariant of an elliptic curve with complex multiplication (CM), and if so, the associated CM discriminant. More generally, given an elliptic curve E over a number field, one can use them to compute the endomorphism ring of E. Our algorithms admit simple implementations that are asymptotically and practically faster than existing approaches.
Software optimization for fast encoding and decoding of Reed-Solomon codes

In this work, we propose a software library written in C for encoding and decoding Reed-Solomon codes. Library consists of one scalar CODEC and two vectorized codecs for x86 architecture. Vectorized codecs use the benefits of SSSE3 or AVX2 instruction sets. We compare the performance of our three codecs with the JPWL RS CODEC from the Open JPEG library. The performance comparison methodology is described, and it is based on the measuring of the encoding and decoding speed. The results demonstrate a 4.1x speed gain with the scalar CODEC and a 19.6x gain with the vectorized CODEC. Based on testing results and supported instruction sets, a dynamic selection of CODEC version is proposed.
Serious game development of COVID-19 social distancing simulator using agent-based modelling

A social distancing protocol has been acknowledged and encouraged practically everywhere, since the global COVID-19 epidemic in 2020. This research resulted in the Serious game to simulate social distance using agent-based modeling so that it can be used as a medium of entertainment and educate the public during the Pandemic COVID-19 conditions by developing simulation games using UNITY 3D to educate in the middle of the COVID-19 pandemic and reduce transmission rates on an individual scale.
Design of a Multi-parameter Adjustable Pseudorandom Code Source

With the continuous development of PRCPM detctor, the accuracy and data transmission rate are increasingly required, so the detctor needs a higher rate of pseudorandom code to drive. This paper designs a high speed pseudorandom code source based on Cyclone 10 GX FPGA and the dynamic reconfiguration of the Nios II processor, its output signal frequency can reach up to 12.5GHz, and supports the dynamic reconfiguration of frequency and m sequence order.
Effluent ammonia nitrogen prediction of wastewater treatment process via Tikhonov regularized echo state network

To predict the effluent ammonia nitrogen $(NH_{4}-N)$, a Tikhonov regularized echo state network (TRESN) is proposed. TRESN uses the Tikhonov regularization method instead of linear regression to train the model, and transforms the selection of Tikhonov regularization parameters into a statistical inference of hyperparameters. The simulation results show that TRESN can well solve effluent $NH_{4}-N$ prediction compared with other ESNs, also has higher prediction accuracy and generalization ability.
A Robust Signal Modulation Recognition Method Against Black-Box Detection Attack

Deep learning (DL) models have been widely used in the recognition of modulation types with outstanding recognition effects. With the improvement of modulation recognition, the perturbation from the attacker has also changed from adding physical interference to the original signal to an adversarial attack based on the neural network. The adversarial attack adding subtle perturbation which is imperceptible to the human eye, makes the neural network produce false recognition results with high confidence. This kind of perturbation is hard to be reflected in spectrogram or constellation diagram, so it is seriously destructive to the modulation recognition algorithm based on neural networks. In response to adversarial attacks, we propose a modulation recognition method against black-box detection attacks. In this paper knowledge distillation is used to defend against the attack that comes from the attacker’s black-box detection. The experimental results demonstrated that the defense method constructed in this paper can improve the ability to defend adversarial samples and keep the recognition accuracy of the recognition network. This article aims at improving the robustness of the network and constructing a robust modulation recognition network.
Certifiably Robust Reinforcement Learning through Model-Based Abstract Interpretation

We present a reinforcement learning (RL) framework in which the learned policy comes with a machine-checkable certificate of provable adversarial robustness. Our approach, called CAROL, learns a model of the environment. In each learning iteration, it uses the current version of this model and an external abstract interpreter to construct a differentiable signal for provable robustness. This signal is used to guide learning, and the abstract interpretation used to construct it directly leads to the robustness certificate returned at convergence. We give a theoretical analysis that bounds the worst-case accumulative reward of CAROL. We also experimentally evaluate CAROL on four MuJoCo environments with continuous state and action spaces. On these tasks, CAROL learns policies that, when contrasted with policies from the state-of-the-art robust RL algorithms, exhibit: (i) markedly enhanced certified performance lower bounds; and (ii) comparable performance under empirical adversarial attacks.
Call for Papers -- The BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus

We present the call for papers for the BabyLM Challenge: Sample-efficient pretraining on a developmentally plausible corpus. This shared task is intended for participants with an interest in small scale language modeling, human language acquisition, low-resource NLP, and cognitive modeling. In partnership with CoNLL and CMCL, we provide a platform for approaches to pretraining with a limited-size corpus sourced from data inspired by the input to children. The task has three tracks, two of which restrict the training data to pre-released datasets of 10M and 100M words and are dedicated to explorations of approaches such as architectural variations, self-supervised objectives, or curriculum learning. The final track only restricts the amount of text used, allowing innovation in the choice of the data, its domain, and even its modality (i.e., data from sources other than text is welcome). We will release a shared evaluation pipeline which scores models on a variety of benchmarks and tasks, including targeted syntactic evaluations and natural language understanding.
Based on spss22.0 statistical software to multiple intelligences experiment in Physical Education

since the new curriculum reform, due to various reasons, there are still many unsatisfactory places in college physical education compared with the requirements of the national physical education curriculum teaching guidelines for colleges and universities). Most college physical education courses are limited to optional courses, compulsory courses, physical education evaluation courses, physical health care and physical education theory courses. These courses can not meet the requirements of the development of students' multiple intelligences. Based on the experimental data of multiple intelligences theory, this paper establishes the way of project teaching and scientific research, explores the teaching approach of multi project content, and carries out the experimental research of the new curriculum. At the practical operation level of the school, build a new, diversified, open, sustainable and optional curriculum system for students, which provides a new basis and path for the development of the new curriculum.