Reviewerly/exHarmony huggingface dataset:
Concept-based Explanations using Non-negative Concept Activation Vectors and Decision Tree for CNN Models

This paper evaluates whether training a decision tree based on concepts extracted from a concept-based explainer can increase interpretability for Convolutional Neural Networks (CNNs) models and boost the fidelity and performance of the used explainer. CNNs for computer vision have shown exceptional performance in critical industries. However, it is a significant barrier when deploying CNNs due to their complexity and lack of interpretability. Recent studies to explain computer vision models have shifted from extracting low-level features (pixel-based explanations) to mid-or high-level features (concept-based explanations). The current research direction tends to use extracted features in developing approximation algorithms such as linear or decision tree models to interpret an original model. In this work, we modify one of the state-of-the-art concept-based explanations and propose an alternative framework named TreeICE. We design a systematic evaluation based on the requirements of fidelity (approximate models to original model's labels), performance (approximate models to ground-truth labels), and interpretability (meaningful of approximate models to humans). We conduct computational evaluation (for fidelity and performance) and human subject experiments (for interpretability) We find that Tree-ICE outperforms the baseline in interpretability and generates more human readable explanations in the form of a semantic tree structure. This work features how important to have more understandable explanations when interpretability is crucial.
Pop-Up Book Media Assisted By QR Code For Second-Grade Elementary School Students

The teacher only uses books in learning activities even though the book has many shortcomings, such as an unattractive presentation. Students also need help understanding each material; more material is presented in the explanation book. This research aims to develop a pop-up book media with the help of a QR Code on the theme of 8 sub-themes 1 for second-grade elementary school. This type of research is the development of the ADDIE model. The research subjects were six experts consisting of four material experts and two media experts. The test subjects were two teacher practitioners and ten students. The data collection method used is a questionnaire. The instrument used to collect data is a rating scale. Data analysis techniques are descriptive qualitative, and quantitative. The study results are the assessments obtained from material experts, namely 94% (very good). The assessment of learning media experts is 95% (very good). The assessment of the practicality of the media given by the teacher is 94% (very good) and 90% of students (very practical). It was concluded that the QR Code Assisted Pop-up book Media received valid and practical qualifications to be suitable for use in learning.
Developing Virtual Reality for English Presentation and Negotiation Skills Training Model

This study is based on a previous study conducted by the researcher in 2020 on 478 actors of Small and Medium Enterprises (SMEs) who joined UMKM Juara and UMKM Binaan Rumah BUMN Bandung communities in West Java, Indonesia.The results of the prior study indicate that the awareness of the importance of mastering English is extremely high among SMEs actors (98%) Their desire to learn English is equally strong (98.4%), and this coincides with the significant interest of SMEs actors in becoming exporters (92.8%).During the development of the English skill six-month training in 2021, a new issue arose.It was revealed that the SMEs actors had a strong desire to gain direct experience in practicing English with native speakers in a more realistic setting for conducting global business, which was deemed unlikely due to the Covid-19 epidemic.Yet, using virtual reality technology, this research conducted the training to improve the SMEs actors' English proficiency, focusing on presentation and negotiation skills.The utilization of virtual reality technology in the training of English skill enhancement was anticipated to provide SMEs actors with an experience that was as close as possible to the actual conditions of conducting global business, thereby encouraging them to be more confident in entering the export market and contributing to the national economic recovery.This article is a conceptual paper in which the research's concept and innovation might be expanded.
Digital Collaboration Network for Coffee Marketing Based on an Agroforestry System for Economic Recovery of Coffee Farmers in Bandung Regency, West Java

In the fourth industrial revolution dimension, digital transformation requires the creation of a "digital collaboration network" (DCN) among stakeholders.This collaborative network aims to strike a balance between competition and sharing, benefiting all parties involved.MSMEs in Indonesia, particularly in the batik business, have embraced DCN and developed a digital community and a new business model that are crisis resistant.However, there has been no report on the DCN application for coffee commodities.The research was conducted from May to November 2021 in Bandung Regency, West Java.This research aimed to understand the application of DCN technology to coffee farming in agroforestrybased coffee growing techniques.The primary goal of this program is to understand a digital community for coffee marketing based on data from the Tenjolaya Forest-Village Non-governmental Organization, consisting of three main networks: ICT, knowledge, and social networks.Coffee farmers could communicate and interact virtually, and this network was also particularly effective in raising their income during the COVID-19 pandemic.Furthermore, coffee producers can strengthen their position in the coffee supply chain by engaging in digital marketing, which allows them to connect with buyers directly.
Achieving Social Justice Through the Role of Technology Higher Education Institution: Sociotechnology Discipline and Technoculture Study Program

In such a massive "politicization" of social and cultural life, it appears that the role of Technological Higher Education Institution (Lembaga Pendidikan Tinggi Teknologi/LPTT) in national development is diminishing and less significant with the decline of the role of technology at the national level.The future challenge of LPTT is to increase the role of technology.This LPTT revitalization is highly possible due to the central role of technology in social and cultural life, so that LPTT is not focused only on technology but should also become the Cultural Development Center.The method used in this research is a qualitative method with a case study in Technological Higher Education Institution (LPTT).This method was used considering the demand for technology to immediately link with the society and culture that ultimately leads to quality human civilization.It is time for LPTT to apply transdisciplinary thinking.As a result, the development of the Sociotechnology discipline is proposed.The discipline is implemented in a higher education process and a new nomenclature is proposed for the study program, which is called Technoculture.The discipline and study program work by constructing the Design Thinking Work Method based on the Modeling and Computation Method.Design Thinking that is applied to Humanity, Technology, and Social Science based on the Modeling and Computation Method is part of Digital Humanities.This new approach, including the Learning Method, develops the human resource output from this discipline, and the study program produces human resources with the frame of thought on Applied Science Branch -Sub Social-Sub Sub Public Affairs that supports Social Welfare.This process leads to human resources as pioneers, motivators, innovators, and creators for achieving Social Justice in the nation.
CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation

Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task. Mainstream approaches follow a multi-stage framework and suffer from high training costs. In this paper, we explore the potential of Contrastive Language-Image Pre-training models (CLIP) to localize different categories with only image-level labels and without further training. To efficiently generate high-quality segmentation masks from CLIP, we propose a novel WSSS framework called CLIP-ES. Our framework improves all three stages of WSSS with special designs for CLIP: 1) We introduce the softmax function into GradCAM and exploit the zero-shot ability of CLIP to suppress the confusion caused by non-target classes and backgrounds. Meanwhile, to take full advantage of CLIP, we re-explore text inputs under the WSSS setting and customize two text-driven strategies: sharpness-based prompt selection and synonym fusion. 2) To simplify the stage of CAM refinement, we propose a real-time class-aware attention-based affinity (CAA) module based on the inherent multi-head self-attention (MHSA) in CLIP-ViTs. 3) When training the final segmentation model with the masks generated by CLIP, we introduced a confidence-guided loss (CGL) focus on confident regions. Our CLIP-ES achieves SOTA performance on Pascal VOC 2012 and MS COCO 2014 while only taking 10% time of previous methods for the pseudo mask generation. Code is available at https://github.com/linyq2117/CLIP-ES.
MonoNeRF: Learning Generalizable NeRFs from Monocular Videos without Camera Pose

We propose a generalizable neural radiance fields - MonoNeRF, that can be trained on large-scale monocular videos of moving in static scenes without any ground-truth annotations of depth and camera poses. MonoNeRF follows an Autoencoder-based architecture, where the encoder estimates the monocular depth and the camera pose, and the decoder constructs a Multiplane NeRF representation based on the depth encoder feature, and renders the input frames with the estimated camera. The learning is supervised by the reconstruction error. Once the model is learned, it can be applied to multiple applications including depth estimation, camera pose estimation, and single-image novel view synthesis. More qualitative results are available at: https://oasisyang.github.io/mononerf .
Worst case complexity bounds for linesearch-type derivative-free algorithms

This paper is devoted to the analysis of worst case complexity bounds for linesearch-type derivative-free algorithms for the minimization of general non-convex smooth functions. We prove that two linesearch-type algorithms enjoy the same complexity properties which have been proved for pattern and direct search algorithms. In particular, we consider two derivative-free algorithms based on two different linesearch techniques and manage to prove that the number of iterations and of function evaluations required to drive the norm of the gradient of the objective function below a given threshold $\epsilon$ is ${\cal O}(\epsilon^{-2})$ in the worst case.
Generalization Ability of Wide Neural Networks on $\mathbb{R}$

We perform a study on the generalization ability of the wide two-layer ReLU neural network on $\mathbb{R}$. We first establish some spectral properties of the neural tangent kernel (NTK): $a)$ $K_{d}$, the NTK defined on $\mathbb{R}^{d}$, is positive definite; $b)$ $\lambda_{i}(K_{1})$, the $i$-th largest eigenvalue of $K_{1}$, is proportional to $i^{-2}$. We then show that: $i)$ when the width $m\rightarrow\infty$, the neural network kernel (NNK) uniformly converges to the NTK; $ii)$ the minimax rate of regression over the RKHS associated to $K_{1}$ is $n^{-2/3}$; $iii)$ if one adopts the early stopping strategy in training a wide neural network, the resulting neural network achieves the minimax rate; $iv)$ if one trains the neural network till it overfits the data, the resulting neural network can not generalize well. Finally, we provide an explanation to reconcile our theory and the widely observed ``benign overfitting phenomenon''.
On Multi-head Ensemble of Smoothed Classifiers for Certified Robustness

Randomized Smoothing (RS) is a promising technique for certified robustness, and recently in RS the ensemble of multiple deep neural networks (DNNs) has shown state-of-the-art performances. However, such an ensemble brings heavy computation burdens in both training and certification, and yet under-exploits individual DNNs and their mutual effects, as the communication between these classifiers is commonly ignored in optimization. In this work, starting from a single DNN, we augment the network with multiple heads, each of which pertains a classifier for the ensemble. A novel training strategy, namely Self-PAced Circular-TEaching (SPACTE), is proposed accordingly. SPACTE enables a circular communication flow among those augmented heads, i.e., each head teaches its neighbor with the self-paced learning using smoothed losses, which are specifically designed in relation to certified robustness. The deployed multi-head structure and the circular-teaching scheme of SPACTE jointly contribute to diversify and enhance the classifiers in augmented heads for ensemble, leading to even stronger certified robustness than ensembling multiple DNNs (effectiveness) at the cost of much less computational expenses (efficiency), verified by extensive experiments and discussions.
Research on restaurant recommendation using machine learning

A recommender system is a system that helps users filter irrelevant information and create user interest models based on their historical records. With the continuous development of Internet information, recommendation systems have received widespread attention in the industry. In this era of ubiquitous data and information, how to obtain and analyze these data has become the research topic of many people. In view of this situation, this paper makes some brief overviews of machine learning-related recommendation systems. By analyzing some technologies and ideas used by machine learning in recommender systems, let more people understand what is Big data and what is machine learning. The most important point is to let everyone understand the profound impact of machine learning on our daily life.
Desain Masterplan Direktorat Sistem Teknologi Informasi Universitas Teknologi Sumbawa Menggunakan Framework Garuda Smart City

The purpose of this study was to design a master plan for the Directorate of Information Systems and Technology, Sumbawa University of Technology (UTS), an educational institution located in Sumbawa, West Nusa Tenggara Province with an area of 60 hectares, has 32 study programs and has 3,772 active students. Master plan create using the smart campus model adopted from the Garuda Smart City framework. The smart campus model consists of three main models, namely smart people, smart governance, and smart infrastructure, technology and environment. The development of intelligent people includes services to Education Personnel (Tendik), services to teaching staff, and services to students. Meanwhile, smart governance refers to a written document issued officially in the form of a circular letter by the Directorate of Systems and Information Technology. The third model, namely smart infrastructure, technology and environment which includes hardware and software. With the draft master plan for the Directorate of Information Systems and Technology, this is one way to realize good quality, efficient and integrated information technology services for the entire academic community of the Sumbawa University of Technology
Analisis Keamanan Website Menggunakan OWASP dan Control Objective For Information and Related Technology (COBIT 5)

The website is a collection of information services that function as a means of interaction either individually, in groups, or in organizations. The benchmark that makes a website good is a website that has a high level of security so that it can provide comfort for its users. Therefore all agencies try to provide the best through websites that are used as information services, one of which is the Super Indo Company. However, in the running process, there are disturbances on the website, such as bugs found and the absence of security standards applied to the website. The purpose of this research is to find alerts so that bugs are identified using the OWASP ZAP application and to find out the level of security applied to websites using the COBIT 5 standard or method. The results obtained from all activity processes get a capability level value of 3.42 or at the Established process level (a management process). The Super Indo company is already at a fairly good level in the website development process
Penerapan Metode Usability Testing dan System Usability Scale untuk Mengevaluasi Website Akademik

A web-based academic information system, developed to address the demands of organizations wishing to computerize their educational offerings to improve efficiency, service excellence, competitiveness and the quality of their workforce. The Syarif Kasim Riau Islamic University Animal Husbandry Study Program is one of the universities that has an academic website. This website was launched in 2013 and last updated in 2017, but its quality has never been assessed. This study aims to assess website usability using the System Usability Scale (SUS) test on the Uin Suska Riau Animal Husbandry Research Program website. In this study, usability testing is done by giving tasks to users to complete, and SUS is used to measure user satisfaction with academic websites. Respondents were taken using a random sampling technique with a total of 85 respondents obtained from the slovin formula. In the ranking of adjectives including ok on the class F scale and including unacceptable in the acceptance range category, namely unacceptable websites, the final result is 50.03 according to SUS calculations. The results of this study include 5 recommendations for improvement solutions as a reference for future website development.
Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement

Object rearrangement has recently emerged as a key competency in robot manipulation, with practical solutions generally involving object detection, recognition, grasping and high-level planning. Goal-images describing a desired scene configuration are a promising and increasingly used mode of instruction. A key outstanding challenge is the accurate inference of matches between objects in front of a robot, and those seen in a provided goal image, where recent works have struggled in the absence of object-specific training data. In this work, we explore the deterioration of existing methods' ability to infer matches between objects as the visual shift between observed and goal scenes increases. We find that a fundamental limitation of the current setting is that source and target images must contain the same $\textit{instance}$ of every object, which restricts practical deployment. We present a novel approach to object matching that uses a large pre-trained vision-language model to match objects in a cross-instance setting by leveraging semantics together with visual features as a more robust, and much more general, measure of similarity. We demonstrate that this provides considerably improved matching performance in cross-instance settings, and can be used to guide multi-object rearrangement with a robot manipulator from an image that shares no object $\textit{instances}$ with the robot's scene.
Removing Image Artifacts From Scratched Lens Protectors

A protector is placed in front of the camera lens for mobile devices to avoid damage, while the protector itself can be easily scratched accidentally, especially for plastic ones. The artifacts appear in a wide variety of patterns, making it difficult to see through them clearly. Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts. Though different methods have been proposed for some specific distortions, they seldom consider such inherent challenges. In our work, we consider the inherent challenges in a unified framework with two cooperative modules, which facilitate the performance boost of each other. We also collect a new dataset from the real world to facilitate training and evaluation purposes. The experimental results demonstrate that our method outperforms the baselines qualitatively and quantitatively. The code and datasets will be released after acceptance.
Guiding Pretraining in Reinforcement Learning with Large Language Models

Reinforcement learning algorithms typically struggle in the absence of a dense, well-shaped reward function. Intrinsically motivated exploration methods address this limitation by rewarding agents for visiting novel states or transitions, but these methods offer limited benefits in large environments where most discovered novelty is irrelevant for downstream tasks. We describe a method that uses background knowledge from text corpora to shape exploration. This method, called ELLM (Exploring with LLMs) rewards an agent for achieving goals suggested by a language model prompted with a description of the agent's current state. By leveraging large-scale language model pretraining, ELLM guides agents toward human-meaningful and plausibly useful behaviors without requiring a human in the loop. We evaluate ELLM in the Crafter game environment and the Housekeep robotic simulator, showing that ELLM-trained agents have better coverage of common-sense behaviors during pretraining and usually match or improve performance on a range of downstream tasks. Code available at https://github.com/yuqingd/ellm.
Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection

The presence of a large number of bots in Online Social Networks (OSN) leads to undesirable social effects. Graph neural networks (GNNs) are effective in detecting bots as they utilize user interactions. However, class-imbalanced issues can affect bot detection performance. To address this, we propose an over-sampling strategy for GNNs (OS-GNN) that generates samples for the minority class without edge synthesis. First, node features are mapped to a feature space through neighborhood aggregation. Then, we generate samples for the minority class in the feature space. Finally, the augmented features are used to train the classifiers. This framework is general and can be easily extended into different GNN architectures. The proposed framework is evaluated using three real-world bot detection benchmark datasets, and it consistently exhibits superiority over the baselines.