Reviewerly/exHarmony huggingface dataset:
Scalable Performance Analysis for Vision-Language Models

Joint vision-language models have shown great performance over a diverse set of tasks. However, little is known about their limitations, as the high dimensional space learned by these models makes it difficult to identify semantic errors. Recent work has addressed this problem by designing highly controlled probing task benchmarks. Our paper introduces a more scalable solution that relies on already annotated benchmarks. Our method consists of extracting a large set of diverse features from a vision-language benchmark and measuring their correlation with the output of the target model. We confirm previous findings that CLIP behaves like a bag of words model and performs better with nouns and verbs; we also uncover novel insights such as CLIP getting confused by concrete words. Our framework is available at https://github.com/MichiganNLP/Scalable-VLM-Probing and can be used with other multimodal models and benchmarks.
Deep Stochastic Mechanics

This paper introduces a novel deep-learning-based approach for numerical simulation of a time-evolving Schr\"odinger equation inspired by stochastic mechanics and generative diffusion models. Unlike existing approaches, which exhibit computational complexity that scales exponentially in the problem dimension, our method allows us to adapt to the latent low-dimensional structure of the wave function by sampling from the Markovian diffusion. Depending on the latent dimension, our method may have far lower computational complexity in higher dimensions. Moreover, we propose novel equations for stochastic quantum mechanics, resulting in linear computational complexity with respect to the number of dimensions. Numerical simulations verify our theoretical findings and show a significant advantage of our method compared to other deep-learning-based approaches used for quantum mechanics.
BRICS: Bi-level feature Representation of Image CollectionS

We present BRICS, a bi-level feature representation for image collections, which consists of a key code space on top of a feature grid space. Specifically, our representation is learned by an autoencoder to encode images into continuous key codes, which are used to retrieve features from groups of multi-resolution feature grids. Our key codes and feature grids are jointly trained continuously with well-defined gradient flows, leading to high usage rates of the feature grids and improved generative modeling compared to discrete Vector Quantization (VQ). Differently from existing continuous representations such as KL-regularized latent codes, our key codes are strictly bounded in scale and variance. Overall, feature encoding by BRICS is compact, efficient to train, and enables generative modeling over key codes using the diffusion model. Experimental results show that our method achieves comparable reconstruction results to VQ while having a smaller and more efficient decoder network (50% fewer GFlops). By applying the diffusion model over our key code space, we achieve state-of-the-art performance on image synthesis on the FFHQ and LSUN-Church (29% lower than LDM, 32% lower than StyleGAN2, 44% lower than Projected GAN on CLIP-FID) datasets.
Dimensionality Reduction for General KDE Mode Finding

Finding the mode of a high dimensional probability distribution $D$ is a fundamental algorithmic problem in statistics and data analysis. There has been particular interest in efficient methods for solving the problem when $D$ is represented as a mixture model or kernel density estimate, although few algorithmic results with worst-case approximation and runtime guarantees are known. In this work, we significantly generalize a result of (LeeLiMusco:2021) on mode approximation for Gaussian mixture models. We develop randomized dimensionality reduction methods for mixtures involving a broader class of kernels, including the popular logistic, sigmoid, and generalized Gaussian kernels. As in Lee et al.'s work, our dimensionality reduction results yield quasi-polynomial algorithms for mode finding with multiplicative accuracy $(1-\epsilon)$ for any $\epsilon > 0$. Moreover, when combined with gradient descent, they yield efficient practical heuristics for the problem. In addition to our positive results, we prove a hardness result for box kernels, showing that there is no polynomial time algorithm for finding the mode of a kernel density estimate, unless $\mathit{P} = \mathit{NP}$. Obtaining similar hardness results for kernels used in practice (like Gaussian or logistic kernels) is an interesting future direction.
The Effects of the Nutrition Lottery Draw Game to Increase Nutrition Knowledge

&lt;p class="abstrak"&gt;&lt;em&gt;A good generation must be created through effort and real work. One generation is a group of schoolchildren who are the nation's investment. A complete generation must be supported by good nutrition. In order to fulfill good food ingredients, of course, it requires proper nutritional knowledge. This study aims to determine the effect of the nutrition lottery draw game media on increasing nutritional knowledge in elementary school children. The research design used a quasi-experimental design (Non-Randomized group pre-test post-test) with research subjects of as many as 48 children divided into 2 (two) treatment groups (each group consisting of 24 people). The subjects of the study employed the students in Elementary School 17 and 20 Siantan, Jungkat District, Mempawah Regency in March 2021. Data analysis with Wilcoxon test and Man-Whithney. Furthermore, the intervention was carried out for one month effectively and divided into 2 (two) groups, namely, one group was provided games treatment and one group had no treatment. There were differences in knowledge scores before and after the intervention in the control and treatment groups. There was no difference in the increase of knowledge scores in the treatment and control groups.&lt;/em&gt;&lt;/p&gt;&lt;p class="abstrak"&gt;&lt;em&gt; &lt;/em&gt;&lt;/p&gt;&lt;p class="abstrak"&gt;&lt;strong&gt;&lt;em&gt;Keywords:&lt;/em&gt;&lt;/strong&gt;&lt;em&gt; &lt;strong&gt;Games, Nutritional Knowledge, Schoolchildren&lt;/strong&gt;&lt;/em&gt;&lt;/p&gt;
Do Smart Contracts Incur Higher Transaction Costs Than Traditional Contracts?

A smart contract is an agreement enforced by blockchain technology. It supposedly allows the parties involved to conduct transactions more efficiently than a traditional contract, which is based on legal (costly) enforcement. This chapter challenges this claim. Given the need for an efficiency-enhancing adaptation of institutional arrangements—a chief problem of Oliver Williamson’s transaction cost economics—smart contracts may incur higher transaction costs than traditional contracts.
Efektivitas Pelatihan Power Point Untuk Meningkatkan Keterampilan Anak Didik Panti Asuhan Sakinah

Power Point training is a training designed to assist participants in mastering the skills in using presentation software, namely Microsoft Power Point. In this training, participants will learn how to create interesting and effective presentations using Power Point. The material taught in Power Point training includes how to create attractive presentation slides using images, text, graphics, tables, and other media. In addition, participants will also be taught how to present information clearly and easily understood by the audience. This training will also discuss good slide layout, the use of animation and transitions, and effective presentation techniques. Participants will learn how to create engaging and memorable presentations using these techniques. By attending Power Point training, participants will be able to improve their presentation skills and create more interesting and effective presentations. Participants will get tips and tricks from the instructor in using Power Point.&#x0D; &#x0D; Keywords: Training, Power Point, Presentation
ELECTRONIC INFORMATION AS EVIDENCE

The widespread use of digital technology both in everyday life and in law enforcement raises the question of the use of digital information in evidence in criminal proceedings. However, there is still no single definition. Thus, quite often the term "digital information" is defined as information in the form of signals of any physical nature, recorded on computer media, the content and / or properties of which establish the presence or absence of circumstances to be proved in criminal proceedings. It is important to understand that such information can be created, transmitted, stored, etc. not only with the help of computer technology, but also with the use of other equipment (dictaphones, digital cameras, camcorders, smartphones, etc.).&#x0D; Analysis of the state of the issue. Digital information is objective. This is due to the fact that its fixation on digital media occurs without processing by human consciousness - in the form in which it objectively existed, regardless of the perception of the person who fixes it. Most often, this information is created outside the framework of criminal proceedings not to bring it to the attention of the pre-trial investigation or court, but for a completely different purpose. For example, CCTV cameras can be installed for security purposes or to prevent and combat crime. Making a video eliminates the participation of the human factor, because the recording of the event is automatic. As a result, the information obtained in this way is objective.&#x0D; The current CPC does not contain the concept of "electronic evidence", but the analysis of other procedural codes (including CAS, CPC and CPC) allows us to establish that this concept means information in electronic (digital) form that contains information about the circumstances relevant to the case , in particular, electronic documents (text documents, graphics, plans, photographs, video and audio recordings, etc.), websites (pages), text, multimedia and voice messages, metadata, databases and other data in electronic form.
Exploring the Intersection of Criminal Laws and Technological Advances: Challenges and Implications

Exploring the Intersection of Criminal Laws and TechnologicalAdvances: Challenges and Implications
The Exploration in Spoken Chinese Teaching Models of Teaching Chinese as a Foreign Language in the Post-Pandemic EraFrom the Perspective of Sociocultural Theory

With its convenience and sharing, online Chinese teaching is gradually becoming the main method of international Chinese teaching in the context of the epidemic. However, at the same time, online Chinese teaching also has some drawbacks, such as poor interactivity, being gradually exam-oriented, and insufficient experience of teaching online. This paper uses the method of literature analysis to analyze the spoken Chinese teaching in Teaching Chinese as a Foreign Language under the perspective of sociocultural theory, and comes up with several pedagogical implications for teaching foreigners Chinese on the Internet. It can be concluded that sociocultural theory can guide the teaching of Chinese as a Foreign Language online and contributes to the development of its teaching mode. From the perspective of sociocultural theory, teachers should insist the principle of taking the student as the main role of class, help them taste specific culture through simulated situational teaching, and accumulate teaching experience, which can also be beneficial to online platform construction.
A Review on Autonomous AI-based robust model of malware detection and classification for 2d images

&lt;p&gt;This paper discusses 2d malware image detection and classifying with AI based Model using Machine Learning. It aims to use previous data and learn and train automatically to identify and classify malware families. Results are based on several parameters. &lt;/p&gt;
Exploring the Use of the Term 'Bao' on the Internet

In recent years, as society continues to change, internet epithets have gone through generations of change. Scholars' enthusiasm for epithets peaked in the last decade but rapidly faded away. This article explores the origins and influence of the Internet address form 'Bao'. It examines the phenomenon of 'virtual intimacy' in the social use of the term 'Bao'. "The term 'Bao' has become popular because of a movie and some hot stems and was initially used in intimate relationships between parents and children or couples. This application has greatly solved the dilemma faced by Internet users and brought people closer to each other while reflecting the diverse psychology of contemporary people in choosing address words in online social networking. The term 'Bao' embodies the principle of economy of language, and the use of a relatively small number of words reflects the great utility of language, injecting new power into online terms of endearment while reflecting the homogenisation of social language. It also provokes reflection on 'virtual intimacy' in a social atmosphere.
Analysis on the Application of Austins Speech Act Theory in the Fort-da Game

Fort-da game is a classic case of psychoanalytic doctrine, and its model has provided great inspiration for the study of psychiatric disorders such as masochism and hysteria. In this paper, whether the motive of the young boy's behavior is hidden behind the fort-da game would be analyzed by applying Austin's speech act theory from the perspective of linguistics and exploring the operation mode of other language games. In conclusion, fort-da could be a kind of speech act that reflects the little boys intention.
The Significance of Kappa and F-score in Clustering Ensemble: A Comprehensive Analysis

Abstract Clustering ensemble techniques have gained significant attention due to their ability to enhance partition results’ accuracy and robustness. Selective clustering ensemble (SCE) and weighted clustering ensemble (WCE) methods further improve performance by selecting and weighting base partitions or clusters based on their diversity and stability. However, striking a balance between these two factors remains challenging. The primary difficulty lies in evaluating the quality of base partitions and clusters. Existing evaluation criteria, such as normalized mutual information (NMI) and its variants, suffer from inherent flaws, including symmetric problem, context meaning problem, and the disregard for small clusters’ importance. To address these limitations, this paper proposes a novel evaluation method that utilizes kappa and F-score. We introduce a new SCE method that employs kappa to select informative base partitions and utilizes F-score to assign weights to clusters based on their stability. Empirical validation on real datasets demonstrates the effectiveness and efficiency of the proposed approach.
APLIKASI PENCARIAN JUDUL TUGAS AKHIR MAHASISWA BERBASIS VISUAL STUDIO 2012 DAN MYSQL

The application to search for student final assignment titles at the Payakumbuh College of Technology is still being done manually, namely written in a book that has been provided by the admin of the computer engineering study program, thus storing the data has not been computerized so that it can result in human error. Therefore, it is felt less effective and efficient. Because in searching for data for the title of the final assignment, you have to look at it one by one so that there are no similarities between other upper-level students, to do the search takes a relatively long time, the data stored in the book can also be lost by the computer engineering study program admin. So there is a need for an application to search for student final assignment titles at the Payakumbuh College of Technology using Visual Studio 2012 and MySQL. The existence of this search application makes it easier for the computer engineering study program admin to see lists of names of students who have taken the title of the final assignment. The admin of the computer engineering study program can make a search in a short time and doesn't take a long time. In addition, the admin of the computer engineering study program can also perform data archiving by inputting the data into this application, be it student data, lecturer data and title data so that it can overcome the loss of data. The data that has been input can also be printed according to annual data and individual data.
AI Based Farmer’s Assistance Chatbot

Abstract: India has established itself as a prominent player in the global agriculture sector and has predominantly relied on an agriculture-based economy. However, a significant number of farmers and individuals involved in agriculture face various challenges due to limited knowledge of agricultural production. These challenges include inadequate farming techniques, insufficient crop materials, improper crop planning, and imbalanced fertilizer usage. This project presents a prototype of a chatbot framework designed to assist individuals, including farmers, in managing their crops and predicting their needs. The chatbot predicts fertilizer requirements, nutrient levels in crops, and provides valuable knowledge to users regarding basic agricultural needs. The structure employs Natural Language Processing and draws information from pre-determined data to deliver responses to user queries. To ensure accuracy, the chatbot analyzes previous interactions and sources data from "The Indian Council of Agricultural Research."
The Effect of E-Learning on Academic Achievement among Students of the Accounting Program in Public Universities in the Kingdom of Saudi Arabia

This research aimed to understand the impact of e-accounting education on the academic achievement of students of the Bachelor of Accounting Program at The College of Business and Economics at Qassim University. The importance of this research lies in the need to understand the effects of e-learning, its contribution to accounting education, and its effect on the academic achievement of accounting students. This research was conducted by analyzing the academic data of the students for the period from 2019 to 2020. In addition, the research used the academic reports to evaluate the electronic teaching process in accounting department, to understand the impact of e-learning on the educational process. A descriptive analytical approach was employed through the review of previous studies in the fields of e-learning and accounting education. Moreover, a comparison was conducted between the students' achievement in traditional learning in 2019 and in e-learning in 2020 using the analysis obtained from Statistical Package for Social Sciences (SPSS). The results of the research proved that e-learning has positive effect on the academic achievement of students of the Bachelor of Accounting Program at Qassim University. The research recommends to include the e-learning system in the accounting department at Qassim University, and to integrate it with traditional education in order to develop the accounting education.
Converting texts into 3d models using AI

This research article explores the intersection of artificial intelligence (AI) and three-dimensional (3D) modeling, specifically the use of AI to convert textual information into 3D models.The article begins by discussing the background and context of AI and 3D modeling, and identifies the problem of manual 3D modeling being time-consuming and inefficient.The purpose and objectives of the study are then outlined, along with the research questions to be addressed.A literature survey is conducted, reviewing 10 research articles on the topic of AI and 3D modeling.The survey highlights the various AI techniques used in 3D modeling and animation, such as machine learning, computer vision, and natural language processing.The survey also discusses the applications of AI in 3D modeling, including scene understanding and localization, cultural heritage modeling, and medical imaging.The research methodology is then presented, including the research design, data collection methods, text processing and analysis techniques, 3D model generation methods, and evaluation criteria.The results and discussions section summarizes the findings of the study, highlighting the effectiveness of using AI to convert text into 3D models.The limitations and challenges of the study are also discussed, including issues related to accuracy, scalability, and computational resources.Finally, the future enhancement section presents potential improvements to the research, such as incorporating more advanced AI techniques, improving the accuracy of 3D models, and addressing the limitations of the study.In conclusion, this article demonstrates the potential of AI to revolutionize the field of 3D modeling by providing a more efficient and automated process.
On the importance of severely testing deep learning models of cognition

Researchers studying the correspondences between Deep Neural Networks (DNNs) and humans often give little consideration to severe testing when drawing conclusions from empirical findings, and this is impeding progress in building better models of minds. We first detail what we mean by severe testing and highlight how this is especially important when working with opaque models with many free parameters that may solve a given task in multiple different ways. Second, we provide multiple examples of researchers making strong claims regarding DNN-human similarities without engaging in severe testing of their hypotheses. Third, we consider why severe testing is undervalued. We provide evidence that part of the fault lies with the review process. There is now a widespread appreciation in many areas of science that a bias for publishing positive results (among other practices) is leading to a credibility crisis, but there seems less awareness of the problem here.
STRATEGI PELAKSANAAN PEMBELAJARAN KOSAKATA BAHASA INGGRIS PADA PESERTA DIDIK USIA DINI DI TK ALIFBA LAMPASEH KOTA BANDA ACEH

This research aims: (1) to examine what strategies are used by kindergarten teachers in implementing English vocabulary learning, (2) to examine strategies for implementing English vocabulary learning carried out by teachers in TK Alifba, and (3) to examine how teachers Alifba Kindergarten evaluates the results of the strategy for implementing the English vocabulary learning process. This research was qualitative research because it only describes the situation and condition of the strategy for implementing the English vocabulary learning process in Alifba Kindergarten. Since only one teacher teaches English, the teacher is taken as the research subject and is called the data source. The instruments used to collect data are observation, interviews, and documentation. The data obtained were analyzed with narrative procedures. The results of the study show that in the strategy for implementing the vocabulary learning process, the Alifba Kindergarten teacher prepares the syllabus and teaching materials per lesson package. Teachers use various strategies in implementing the learning process and these strategies include singing, playing roles, and through picture cards. Furthermore, in evaluating the results of the implementation of the teaching and learning process, the teacher evaluates students using one type of test, namely the walking process test. The tests are carried out individually.