Reviewerly/exHarmony huggingface dataset:
Pengaruh Penggunaan Model Pembelajaran Air (Auditory, Intellectualy, Repetition) Terhadap Hasil Belajar Mata Pelajaran Matematika Pada Siswa Kelas IV SDN Nusa Tenggara

The main problem in this study is the learning process that still uses conventional methods, namely lectures and questions and answers, so that it affects the low motivation of students in learning, especially in mathematics subjects, this has an impact on low learning outcomes that have not reached the minimum completion criteria (KKM) of mathematics subjects. This type of research is quantitative research, this research is shown to test the theory through measuring variables with numbers and carrying out data analysis with statistical procedures. The purpose of this study is to find out how the use of the AIR Learning Model (Auditory, Intellectually, Repetition) affects the Learning Outcomes of Mathematics Subjects in Grade IV Students of SDN Nusa Tenggara. Research data were obtained by conducting observations, documentation, and interviews. To test whether there is an influence from the application of the AIR learning method, researchers conducted pre-tests and post-tests. The results of learning mathematics students before the material repetition method was applied, namely the average pre-test obtained an average score of 58.13 and after being given treatment the average score of the post-test was 86.20. From the results of the SPSS analysis, the paired test of the t-test sample obtained a significant value of 0.000 &gt; 0.05 which means that there is an increase in the learning outcomes of the experiment class from the pre-test and post-test.
"Melatonin": A Case Study on AI-induced Musical Style

Although the use of AI tools in music composition and production is steadily increasing, as witnessed by the newly founded AI song contest, analysis of music produced using these tools is still relatively uncommon as a mean to gain insight in the ways AI tools impact music production. In this paper we present a case study of "Melatonin", a song produced by extensive use of BassNet, an AI tool originally designed to generate bass lines. Through analysis of the artists' work flow and song project, we identify style characteristics of the song in relation to the affordances of the tool, highlighting manifestations of style in terms of both idiom and sound.
Sequence Prediction Under Missing Data : An RNN Approach Without Imputation

Missing data scenarios are very common in ML applications in general and time-series/sequence applications are no exceptions. This paper pertains to a novel Recurrent Neural Network (RNN) based solution for sequence prediction under missing data. Our method is distinct from all existing approaches. It tries to encode the missingness patterns in the data directly without trying to impute data either before or during model building. Our encoding is lossless and achieves compression. It can be employed for both sequence classification and forecasting. We focus on forecasting here in a general context of multi-step prediction in presence of possible exogenous inputs. In particular, we propose novel variants of Encoder-Decoder (Seq2Seq) RNNs for this. The encoder here adopts the above mentioned pattern encoding, while at the decoder which has a different structure, multiple variants are feasible. We demonstrate the utility of our proposed architecture via multiple experiments on both single and multiple sequence (real) data-sets. We consider both scenarios where (i)data is naturally missing and (ii)data is synthetically masked.
Ered: Enhanced Text Representations with Entities and Descriptions

External knowledge,e.g., entities and entity descriptions, can help humans understand texts. Many works have been explored to include external knowledge in the pre-trained models. These methods, generally, design pre-training tasks and implicitly introduce knowledge by updating model weights, alternatively, use it straightforwardly together with the original text. Though effective, there are some limitations. On the one hand, it is implicit and only model weights are paid attention to, the pre-trained entity embeddings are ignored. On the other hand, entity descriptions may be lengthy, and inputting into the model together with the original text may distract the model's attention. This paper aims to explicitly include both entities and entity descriptions in the fine-tuning stage. First, the pre-trained entity embeddings are fused with the original text representation and updated by the backbone model layer by layer. Second, descriptions are represented by the knowledge module outside the backbone model, and each knowledge layer is selectively connected to one backbone layer for fusing. Third, two knowledge-related auxiliary tasks, i.e., entity/description enhancement and entity enhancement/pollution task, are designed to smooth the semantic gaps among evolved representations. We conducted experiments on four knowledge-oriented tasks and two common tasks, and the results achieved new state-of-the-art on several datasets. Besides, we conduct an ablation study to show that each module in our method is necessary. The code is available at https://github.com/lshowway/Ered.
Data-access performance anti-patterns in data-intensive systems

Data-intensive systems handle variable, high volume, and high-velocity data generated by human and digital devices. Like traditional software, data-intensive systems are prone to technical debts introduced to cope-up with the pressure of time and resource constraints on developers. Data-access is a critical component of data-intensive systems as it determines the overall performance and functionality of such systems. While data access technical debts are getting attention from the research community, technical debts affecting the performance, are not well investigated. Objective: Identify, categorize, and validate data access performance issues in the context of NoSQL-based and polyglot persistence data-intensive systems using qualitative study. Method: We collect issues from NoSQL-based and polyglot persistence open-source data-intensive systems and identify data access performance issues using inductive coding and build a taxonomy of the root causes. Then, we validate the perceived relevance of the newly identified performance issues using a developer survey.
Mind the Gap in Distilling StyleGANs

StyleGAN family is one of the most popular Generative Adversarial Networks (GANs) for unconditional generation. Despite its impressive performance, its high demand on storage and computation impedes their deployment on resource-constrained devices. This paper provides a comprehensive study of distilling from the popular StyleGAN-like architecture. Our key insight is that the main challenge of StyleGAN distillation lies in the output discrepancy issue, where the teacher and student model yield different outputs given the same input latent code. Standard knowledge distillation losses typically fail under this heterogeneous distillation scenario. We conduct thorough analysis about the reasons and effects of this discrepancy issue, and identify that the mapping network plays a vital role in determining semantic information of generated images. Based on this finding, we propose a novel initialization strategy for the student model, which can ensure the output consistency to the maximum extent. To further enhance the semantic consistency between the teacher and student model, we present a latent-direction-based distillation loss that preserves the semantic relations in latent space. Extensive experiments demonstrate the effectiveness of our approach in distilling StyleGAN2 and StyleGAN3, outperforming existing GAN distillation methods by a large margin.
Plascua: Programming Language Support for Continuous User Authentication

Compared to traditional user authentication methods, continuous user authentication (CUA) provide enhanced protection, guarantees against unauthorized access and improved user experience. However, developing effective continuous user authentication applications using the current programming languages is a daunting task mainly because of lack of abstraction methods that support continuous user authentication. Using the available language abstractions developers have to write the CUA concerns (e.g., extraction of behavioural patterns and manual checks of user authentication) from scratch resulting in unnecessary software complexity and are prone to error. In this paper, we propose new language features that support the development of applications enhanced with continuous user authentication. We develop Plascua, a continuous user authentication language extension for event detection of user bio-metrics, extracting of user patterns and modelling using machine learning and building user authentication profiles. We validate the proposed language abstractions through implementation of example case studies for CUA.
Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning

Automatically discovering failures in vision models under real-world settings remains an open challenge. This work demonstrates how off-the-shelf, large-scale, image-to-text and text-to-image models, trained on vast amounts of data, can be leveraged to automatically find such failures. In essence, a conditional text-to-image generative model is used to generate large amounts of synthetic, yet realistic, inputs given a ground-truth label. Misclassified inputs are clustered and a captioning model is used to describe each cluster. Each cluster's description is used in turn to generate more inputs and assess whether specific clusters induce more failures than expected. We use this pipeline to demonstrate that we can effectively interrogate classifiers trained on ImageNet to find specific failure cases and discover spurious correlations. We also show that we can scale the approach to generate adversarial datasets targeting specific classifier architectures. This work serves as a proof-of-concept demonstrating the utility of large-scale generative models to automatically discover bugs in vision models in an open-ended manner. We also describe a number of limitations and pitfalls related to this approach.
THEORETICAL ASPECTS OF FORMATION OF PROFESSIONAL COMPETENCE OF FUTURE ENGINEERS-TEACHERS OF COMPUTER PROFILE IN THE FIELD OF INFORMATION SYSTEMS SECURITY

The article examines the different approaches of scientists to determine the essence of professional competence of future engineers-teachers of computer science in the field of information systems security, reveals the main approaches to the process of its formation. It is noted that the professional competence of future computer engineers in the field of information systems security should be understood as an integrated characteristic that determines their real ability to identify potential threats to the information educational environment of vocational education institutions and students, prevent unauthorized access to information. information flows, identify risk factors and potential threats, choose effective means of information protection in vocational and industrial institutions, independently solve typical and atypical professional problems and situations related to identifying potential threats to educational and industrial information environment and students. Much attention is paid to the formation of professional competence of future computer engineers in the field of information systems security, which is considered a component of their training, a complex process aimed at mastering sustainable practical skills based on motives, values, professionally important qualities personality, professional knowledge in the field of information systems security, which is manifested in effective professional activities and due to specific circumstances and situations. It is emphasized that professional competence in the field of information systems security should be related to the ability of future computer engineers to identify potential threats to the information educational environment of vocational education institutions and students, prevent unauthorized access to information, navigate information flows, identify risk factors and potential threats, choose effective means of information protection (moral and ethical, administrative and organizational, physical, technical, etc.), etc. Key words: information systems security, pedagogical engineer, professional training, professional competence, professional competence of computer engineers in the field of information systems security, formation of professional competence of computer engineers in the field of information systems security.
Privacy-Preserving AutoML.

philosophy of first and foremost preserving privacy, but doing so in a way that balances security with accessibility and usability of the data for legitimate research. This paper provides details about the TSDC approach toward achieving these goals, which has included creating a secure enclave with no external access for backing up and processing raw data, a publicly accessible website for downloading cleansed data, and a secure portal environment through which approved users can work with detailed spatial data using a variety of tools and reference information. This paper also describes lessons learned from operating the TSDC with respect to improvements in GPS data handling, processing, and user support, along with plans for continual enhancements to better support the needs of both data providers and users and to thus advance the research value derived from such valuable data.
Efficient data-driven gap filling of satellite image time series using deep neural networks with partial convolutions

The abundance of gaps in satellite image time series often complicates the application of deep learning models such as convolutional neural networks for spatiotemporal modeling. Based on previous work in computer vision on image inpainting, this paper shows how three-dimensional spatiotemporal partial convolutions can be used as layers in neural networks to fill gaps in satellite image time series. To evaluate the approach, we apply a U-Net-like model on incomplete image time series of quasi-global carbon monoxide observations from the Sentinel-5P satellite. Prediction errors were comparable to two considered statistical approaches while computation times for predictions were up to three orders of magnitude faster, making the approach applicable to process large amounts of satellite data. Partial convolutions can be added as layers to other types of neural networks, making it relatively easy to integrate with existing deep learning models. However, the approach does not quantify prediction errors and further research is needed to understand and improve model transferability. The implementation of spatiotemporal partial convolutions and the U-Net-like model is available as open-source software.
Exploring and Exploiting Multi-Granularity Representations for Machine Reading Comprehension

Recently, the attention-enhanced multi-layer encoder, such as Transformer, has been extensively studied in Machine Reading Comprehension (MRC). To predict the answer, it is common practice to employ a predictor to draw information only from the final encoder layer which generates the coarse-grained representations of the source sequences, i.e., passage and question. The analysis shows that the representation of source sequence becomes more coarse-grained from finegrained as the encoding layer increases. It is generally believed that with the growing number of layers in deep neural networks, the encoding process will gather relevant information for each location increasingly, resulting in more coarse-grained representations, which adds the likelihood of similarity to other locations (referring to homogeneity). Such phenomenon will mislead the model to make wrong judgement and degrade the performance. In this paper, we argue that it would be better if the predictor could exploit representations of different granularity from the encoder, providing different views of the source sequences, such that the expressive power of the model could be fully utilized. To this end, we propose a novel approach called Adaptive Bidirectional Attention-Capsule Network (ABA-Net), which adaptively exploits the source representations of different levels to the predictor. Furthermore, due to the better representations are at the core for boosting MRC performance, the capsule network and self-attention module are carefully designed as the building blocks of our encoders, which provides the capability to explore the local and global representations, respectively. Experimental results on three benchmark datasets, i.e., SQuAD 1.0, SQuAD 2.0 and COQA, demonstrate the effectiveness of our approach. In particular, we set the new state-of-the-art performance on the SQuAD 1.0 dataset
Exploiting Sentiment and Common Sense for Zero-shot Stance Detection

The stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot settings, we propose to boost the transferability of the stance detection model by using sentiment and commonsense knowledge, which are seldom considered in previous studies. Our model includes a graph autoencoder module to obtain commonsense knowledge and a stance detection module with sentiment and commonsense. Experimental results show that our model outperforms the state-of-the-art methods on the zero-shot and few-shot benchmark dataset--VAST. Meanwhile, ablation studies prove the significance of each module in our model. Analysis of the relations between sentiment, common sense, and stance indicates the effectiveness of sentiment and common sense.
Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members

In many multi-agent settings, participants can form teams to achieve collective outcomes that may far surpass their individual capabilities. Measuring the relative contributions of agents and allocating them shares of the reward that promote long-lasting cooperation are difficult tasks. Cooperative game theory offers solution concepts identifying distribution schemes, such as the Shapley value, that fairly reflect the contribution of individuals to the performance of the team or the Core, which reduces the incentive of agents to abandon their team. Applications of such methods include identifying influential features and sharing the costs of joint ventures or team formation. Unfortunately, using these solutions requires tackling a computational barrier as they are hard to compute, even in restricted settings. In this work, we show how cooperative game-theoretic solutions can be distilled into a learned model by training neural networks to propose fair and stable payoff allocations. We show that our approach creates models that can generalize to games far from the training distribution and can predict solutions for more players than observed during training. An important application of our framework is Explainable AI: our approach can be used to speed-up Shapley value computations on many instances.
Profiler: Profile-Based Model to Detect Phishing Emails

Email phishing has become more prevalent and grows more sophisticated over time. To combat this rise, many machine learning (ML) algorithms for detecting phishing emails have been developed. However, due to the limited email data sets on which these algorithms train, they are not adept at recognising varied attacks and, thus, suffer from concept drift; attackers can introduce small changes in the statistical characteristics of their emails or websites to successfully bypass detection. Over time, a gap develops between the reported accuracy from literature and the algorithm's actual effectiveness in the real world. This realises itself in frequent false positive and false negative classifications. To this end, we propose a multidimensional risk assessment of emails to reduce the feasibility of an attacker adapting their email and avoiding detection. This horizontal approach to email phishing detection profiles an incoming email on its main features. We develop a risk assessment framework that includes three models which analyse an email's (1) threat level, (2) cognitive manipulation, and (3) email type, which we combine to return the final risk assessment score. The Profiler does not require large data sets to train on to be effective and its analysis of varied email features reduces the impact of concept drift. Our Profiler can be used in conjunction with ML approaches, to reduce their misclassifications or as a labeller for large email data sets in the training stage. We evaluate the efficacy of the Profiler against a machine learning ensemble using state-of-the-art ML algorithms on a data set of 9000 legitimate and 900 phishing emails from a large Australian research organisation. Our results indicate that the Profiler's mitigates the impact of concept drift, and delivers 30% less false positive and 25% less false negative email classifications over the ML ensemble's approach.
Development of User Interface/User Experience using Design Thinking Approach for GMS Service Company

Information systems have an important role for firms to increase productivity. A service company located in Bontang, East Kalimantan, Indonesia (GMS), is engaged in scaffolding installation services and scaffolding rentals. The company has steadily experienced annual increase in the number of customers over the years. However, due to the still in manual-way of managing the scaffolding material, the increase of workload has also led to the increasing number of material handling human error cases and inefficient service processing time. As a company that provides B2B services, the management (user) then needs a website-based system for scaffolding material management that is tailored to their service operations and suits their special needs to support their operation. Thus, this study analyzes the suitable user interface/user experience using the Design Thinking method, which consists of 5 steps, namely Empathize, Define, Ideate, Prototype and Test. It serves as the initial phase of information system building to make sure that the design of the website based information system will suit the needs of their management. After conducting empathize stage using interview techniques and questionnaires and also conducting the define stage to gain insights on the required needs, it was known that the website system needed particular features, namely the availability of customer data, material data, user data, work partner data, transaction reports, invoices, integration with whatsapp and email, as well as a dashboard to visualize data such as the amount of material rented, available materials, and transaction history. In the prototype development stage, online UI/UX design tools are used. Using the prototype, the testing stage was then carried out, using the System Usability Scale (SUS) method. As a result, a value of 80.86 was obtained, meaning that the system created has good usability and can be used on an ongoing basis.