Reviewerly/exHarmony huggingface dataset:
GANs Settle Scores!

Generative adversarial networks (GANs) comprise a generator, trained to learn the underlying distribution of the desired data, and a discriminator, trained to distinguish real samples from those output by the generator. A majority of GAN literature focuses on understanding the optimality of the discriminator through integral probability metric (IPM) or divergence based analysis. In this paper, we propose a unified approach to analyzing the generator optimization through variational approach. In $f$-divergence-minimizing GANs, we show that the optimal generator is the one that matches the score of its output distribution with that of the data distribution, while in IPM GANs, we show that this optimal generator matches score-like functions, involving the flow-field of the kernel associated with a chosen IPM constraint space. Further, the IPM-GAN optimization can be seen as one of smoothed score-matching, where the scores of the data and the generator distributions are convolved with the kernel associated with the constraint. The proposed approach serves to unify score-based training and existing GAN flavors, leveraging results from normalizing flows, while also providing explanations for empirical phenomena such as the stability of non-saturating GAN losses. Based on these results, we propose novel alternatives to $f$-GAN and IPM-GAN training based on score and flow matching, and discriminator-guided Langevin sampling.
Harnessing large-language models to generate private synthetic text

Differentially private training algorithms like DP-SGD protect sensitive training data by ensuring that trained models do not reveal private information. An alternative approach, which this paper studies, is to use a sensitive dataset to generate synthetic data that is differentially private with respect to the original data, and then non-privately training a model on the synthetic data. Doing so has several advantages: synthetic data can be reused for other tasks (including for hyper parameter tuning), retained indefinitely, and shared with third parties without sacrificing privacy. However, generating private synthetic data is much harder than training a private model. To improve performance on text data, recent work has utilized public data by starting with a pre-trained generative language model and privately fine-tuning it on sensitive data. This model can be used to sample a DP synthetic dataset. While this strategy seems straightforward, executing it has proven problematic. Previous approaches either show significant performance loss, or have, as we show, critical design flaws. In this paper we demonstrate that a proper training objective along with tuning fewer parameters results in excellent DP synthetic data quality. Our approach is competitive with direct DP-training of downstream classifiers in terms of performance on downstream tasks. Further, we demonstrate that our DP synthetic data is not only useful for downstream classifier training, but also to tune those same models.
Streaming algorithms for evaluating noisy judges on unlabeled data -- binary classification

The evaluation of noisy binary classifiers on unlabeled data is treated as a streaming task: given a data sketch of the decisions by an ensemble, estimate the true prevalence of the labels as well as each classifier's accuracy on them. Two fully algebraic evaluators are constructed to do this. Both are based on the assumption that the classifiers make independent errors. The first is based on majority voting. The second, the main contribution of the paper, is guaranteed to be correct. But how do we know the classifiers are independent on any given test? This principal/agent monitoring paradox is ameliorated by exploiting the failures of the independent evaluator to return sensible estimates. A search for nearly error independent trios is empirically carried out on the \texttt{adult}, \texttt{mushroom}, and \texttt{two-norm} datasets by using the algebraic failure modes to reject evaluation ensembles as too correlated. The searches are refined by constructing a surface in evaluation space that contains the true value point. The algebra of arbitrarily correlated classifiers permits the selection of a polynomial subset free of any correlation variables. Candidate evaluation ensembles are rejected if their data sketches produce independent estimates too far from the constructed surface. The results produced by the surviving ensembles can sometimes be as good as 1\%. But handling even small amounts of correlation remains a challenge. A Taylor expansion of the estimates produced when independence is assumed but the classifiers are, in fact, slightly correlated helps clarify how the independent evaluator has algebraic `blind spots'.
Broadcasting in random recursive dags

A uniform $k$-{\sc dag} generalizes the uniform random recursive tree by picking $k$ parents uniformly at random from the existing nodes. It starts with $k$ ''roots''. Each of the $k$ roots is assigned a bit. These bits are propagated by a noisy channel. The parents' bits are flipped with probability $p$, and a majority vote is taken. When all nodes have received their bits, the $k$-{\sc dag} is shown without identifying the roots. The goal is to estimate the majority bit among the roots. We identify the threshold for $p$ as a function of $k$ below which the majority rule among all nodes yields an error $c+o(1)$ with $c<1/2$. Above the threshold the majority rule errs with probability $1/2+o(1)$.
DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model

Observing the close relationship among panoptic, semantic and instance segmentation tasks, we propose to train a universal multi-dataset multi-task segmentation model: DaTaSeg.We use a shared representation (mask proposals with class predictions) for all tasks. To tackle task discrepancy, we adopt different merge operations and post-processing for different tasks. We also leverage weak-supervision, allowing our segmentation model to benefit from cheaper bounding box annotations. To share knowledge across datasets, we use text embeddings from the same semantic embedding space as classifiers and share all network parameters among datasets. We train DaTaSeg on ADE semantic, COCO panoptic, and Objects365 detection datasets. DaTaSeg improves performance on all datasets, especially small-scale datasets, achieving 54.0 mIoU on ADE semantic and 53.5 PQ on COCO panoptic. DaTaSeg also enables weakly-supervised knowledge transfer on ADE panoptic and Objects365 instance segmentation. Experiments show DaTaSeg scales with the number of training datasets and enables open-vocabulary segmentation through direct transfer. In addition, we annotate an Objects365 instance segmentation set of 1,000 images and will release it as a public benchmark.
Teaching Grammar at the Tertiary Level in the Context of Bangladesh

Experience of teaching English in Bangladesh exhibits that grammar is the least interesting learning item in classes. Students are taught fixed grammatical rules as a compulsory subject from the primary to tertiary level of education as mentioned in the syllabus. As a result, learners of English remain quite weak in grammar; they cannot use appropriate structures of grammar in spoken or written English even though they have completed English language courses at various educational institutions. The paper gives an effort to investigate the reasons for students’ failure in developing this linguistic skill of accuracy. To explore the subject matter of the study, small-scale data has been collected in the form of questionnaires and interviews both from the teachers and students of some renowned private universities in Bangladesh. The study tries to find out some probable solutions to improve the current level of learners in grammar. The study also identifies and examines that English teachers often face some challenges in teaching grammar namely lack of contextual interpretation of grammatical rules, rarely introduced new teaching methods, lack of experience, expectations of the students, lack of facilities, negative perceptions of the teaching of grammar and preparing grammar lessons. The researchers have put forward some pragmatic suggestions to overcome the existing barriers and challenges which will help the English language teacher to teach grammar according to the necessity of the students. The present study may serve as a good insight for other English lecturers and policymakers in overcoming the challenges regarding grammar teaching. The information have been collected from only a few private universities in Dhaka and Feni city. More in-depth study is essential to deal with the problems confidently.
SENTIMENT ANALYSIS BASED ON MACHINE LEARNING METHODS ON TWITTER DATA USING oneAPI

With the gradual development of technology, the use of digital social networks has becomewidespread. As a result of this increased use, the information sharing process has accelerated and increased.This has created an enormous amount of data on digital social networks. With the increasing amount ofdata, it has made it possible to make inferences about people, products, companies and many other areas.Various fields of study have emerged to process and analyze the data. One of these fields of study issentiment analysis. Sentiment analysis is the analysis of data from different sources such as text, sound orimage by classifying the attitude towards a subject as positive, negative or neutral. In this study, sentimentanalysis was performed using Sentiment140 dataset created through Twitter application, one of the digitalsocial network platforms. After preprocessing on the dataset containing English Tweet messages, BernoulliNaive Bayes, Linear Support Vector Machine, Logistic Regression and artificial neural network algorithmsLSTM and CNN methods were used in a hybrid way. Classification results were evaluated with f1 score.Accuracy rates of 0.80%, 0.82%, 83%, 85% were achieved for Bernoulli Naive Bayes, Linear SupportVector Machine, Logistic Regression, LSTM and CNN classifications respectively.
elBERto: Self-supervised Commonsense Learning for Question Answering

Commonsense question answering requires reasoning about everyday situations and causes and effects implicit in context. Typically, existing approaches first retrieve external evidence and then perform commonsense reasoning using these evidence. In this paper, we propose a Self-supervised Bidirectional Encoder Representation Learning of Commonsense (elBERto) framework, which is compatible with off-the-shelf QA model architectures. The framework comprises five self-supervised tasks to force the model to fully exploit the additional training signals from contexts containing rich commonsense. The tasks include a novel Contrastive Relation Learning task to encourage the model to distinguish between logically contrastive contexts, a new Jigsaw Puzzle task that requires the model to infer logical chains in long contexts, and three classic SSL tasks to maintain pre-trained models language encoding ability. On the representative WIQA, CosmosQA, and ReClor datasets, elBERto outperforms all other methods, including those utilizing explicit graph reasoning and external knowledge retrieval. Moreover, elBERto achieves substantial improvements on out-of-paragraph and no-effect questions where simple lexical similarity comparison does not help, indicating that it successfully learns commonsense and is able to leverage it when given dynamic context.
Mobile Application mScheduler

Operating system Android contains database management system (DBMS) SQLite. It means that this database management system is on every smartphone. Mobile application mRaspored (mScheduler) uses local database, which is managed by SQLite. This mobile application is intended for keeping a schedule of classes on a smartphone. It enables viewing, adding and update classes and class schedules. It is written for the Android mobile operating system, which is based on the JAVA programming language. The application supports Android from version 25 up to the latest.
Graphical Consensus-based Sharding for Efficient and Secure Sharings in Blockchain-enabled Internet of Vehicles

&lt;p&gt;Blockchain-enabled Internet of Vehicles provides a reliable collaboration environment for traffic entities and promotes road safety and traffic efficiency through real-time sharings between vehicles and infrastructure. However, this is usually confronted by a bottleneck as the Internet of Vehicles scales up. In this paper, we propose a novel Graphical Consensus-based Sharding (GCS) framework, which is underpinned by four important strategies: 1) A graphical consensus is adopted as its intra-shard consensus, where the consensus group is set up according to its maximal connected subgraph, and the leader is elected by its reliability weight. The consensus group is refreshed intermittently by alternating the role of leader. 2) Within GCS, the intra-shard data are stored in the local chain, while a block-based directed acyclic graph, rather than a chained structure, is employed as the main chain. The local chain is used to respond to requests within each shard, and the main chain supports the cross-shard sharings. GCS will parallelly optimize the throughput of the blockchain-enabled Internet of Vehicles. 3) GCS further introduces the shard backup and node scheduling to handle shard failure and overheating by using new backup strategies and temporal-spatial graph convolutional network prediction model, respectively. 4) An off-chain transmission algorithm is presented for secure sharings between the infrastructure and the vehicles. Simulation results show that the number of Transactions Per Second is 1.69 times higher than that of the non-sharding blockchain, and the pending time is dramatically reduced compared to the mainstreaming sharding approach, which is 1.02s. &lt;/p&gt;
Utilization of Design Application for Mufradat Class X MTS Baabusalam Learning

The quality of learning comes from learning that is designed and implemented by educators. An educator as the spearhead in the implementation of education is very influential in the learning process. Teaching and learning process activities One of the components that support learning so that it is carried out properly and regularly through learning media. Learning media as an intermediary between educators and students to understand learning more effectively and efficiently. The results of interviews with educators were very extraordinary at MTS Baabussalam during the Covid-19 pandemic, in the implementation of online learning, namely students were challenged to understand the teaching materials provided by educators in supporting the education of students, therefore students are required to learn through parental guidance at home. Learning media can be developed with interesting applications to support online learning. The approach between educators and students used in PKM program activities includes development training on learning media, one of which is using the Canva application which includes explanations about the Canva application, the history of the Canva application, the development of learning media for the Canva application, discussions and information exchange. Based on the results of training in making learning media, the Canva application greatly optimizes online learning for educators there. So, in pulling to the conclusion, the method that is my focus at this time is a qualitative method, which means research that focuses on analyzing events where the current research uses a theoretical basis that is factual in the field.
Psychological aspects of the formation and development of algorithmic thinking

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
Real-Time Scene Uyghur Text Detection Network Based on Feature Complementation

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Transition to multilevel higher education: challenges and perspectives

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
The lecture as a component of the educational process in medical biology

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
Innovative activity as an important professional quality of a teacher

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
Content and structure of the concept “Development of intellectual and creative abilities of future music teachers”

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
Multicultural education as a key to the formation of intercultural competence of future teachers

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
Increasing the motivation of ecological and geographical, educational and cognitive activities of students in the lessons of natural sciences

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
Basic approaches to understanding social intelligence in modern psychological science

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
The influence of modern distance education technologies on the formation of students' stress resistance during the war

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
The modern integrated textbook as a method of identifying and developing the vital potential of educational process participants

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
The essence of pedagogical humanism of the future speech therapist

International scientific conference «Research activities and achievements in pedagogy and psychology» : conference proceedings (April 5–6, 2023. Częstochowa, the Republic of Poland). Riga, Latvia : «Baltija Publishing», 2023. 188 pages.
Meta Analysis Study of Marketing Innovation on SME Business Performance in Ethiopia

This study looked at how market innovation affected SMEs' performance in Addis Abeba, Ethiopia, with the use of government support programmes as a moderator. The study's meta-analysis will be guided by the Schumpeter innovation theory, which also incorporates the diffusion of innovation theory, expectancy theory, institutional theory, stakeholder theory, absorptive capacity theory, resource-based view/theory, dynamic capability theory, R-A theory, and unified theory of acceptance and use of technology. The researcher used an effect size approach based on a forest and funnel plot to scan, exclude, and include relevant material. Researchers discovered a connection between the Dimension of Innovation and company performance on the basis of an extensive literature review. Also Based on the studied literature, the researcher discovered a link between the moderation of the government support programme and the performance of SMEs' firms in the area of innovation. To carry out this study, an exploratory sequential mixed research design will be used. Based on the current literature, the study attempts to create a conceptual framework and testable hypotheses. It was discovered that the impact of marketing innovation on a firm's performance was moderated by government support programmes. Businesses must be encouraged to adopt government support programmes with a moderating influence as a result.
Prosedur Manajemen Arsip pada Divisi Penjualan PT. Astra Motor Center Semarang

Archives as memory centers are documents that are very important as evidence of activities or transactions. The purpose of this research is that the writer knows the archive management process that exists at PT. Astra Motor Center Semarang. The research method used is descriptive qualitative method, with data collection techniques through field observations, interviews, and literature study. The results of this study resulted in a record management process at PT. Astra Motor Center Semarang is not running well. It can be concluded that at PT. Astra Motor Center Semarang in carrying out archive management is still not in accordance with the SOP that applies to the company.
Analisis Informasi Status Keberangkatan Penerbangan Oleh Unit Customer Service PT. Angkasa Pura II Bandar Udara Internasional Husein Sastranegara Bandung

Process of providing information unit Customer Service Husein Sastranegara International Airport experienced a discrepancy in flight status information between FIDS and AAS at the departure untill passengers were confused because they received different information from each of the information facilities. Purpose this study is determine process of services provided, constraints, and efforts in the process of information services provided. This research was conducted using a qualitative analysis, which tried to describe all conditions that existed, condition of symptoms according to what they were at the time research was conducted. Based on the main problem, namely to analyze the constraints and what efforts are made by information unit Customer Service when there is a difference in scheduled departure flight information. Result of the study show that the flow of service information on status of departure exists in the unit Customer Service Husein Sastranegara International Airport refers to Standard Operating Procedure of PT. Angkasa Pura II 2022 with the aim of providing fast, precise and accurate information services. Constraints on differences in information caused by miscommunication with other unit and less information service staff. Listening detail every Announcement, contacting related units, teamwork and additional personnel are efforts to overcome the obstacles encountered.