Reviewerly/exHarmony huggingface dataset:
Analisa dan Perancangan Jaringan WAN dengan Sisco Gateway Load Balancing Protocol (GLBP) Menggunakan Simulator GNS3

Lines of communication cannot be separated from the various problems that can interfere with data traffic&#x0D; in it. One problem that arises is the rupture that caused the destruction of communication lines or fiber&#x0D; optic cable intermediary.With a high level of reliability demanded data on the company's lines of&#x0D; communication so that the need for technology that can provide solutions to problems in the network data&#x0D; communication lines. It is very necessary especially companies manage databases and the data is very&#x0D; important to maintain.The use of gateway load balancing technology (GLBP) of cisco is expected to provide&#x0D; data communications reliability in need in the company.Gateway Load Balancing Protocol is the&#x0D; technology division of the traffic load of data to each gateway router or a group who are members of the&#x0D; GLBP. This technology can also divert traffic data gateway router it is damaged or dies communication&#x0D; path to the gateway are functioning properly. GLBP is an improvement on previous technology hot standby&#x0D; routing protocol (HSRP) to provide load balancing functionality.&#x0D;
Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning

Contrastive learning (CL), a self-supervised learning approach, can effectively learn visual representations from unlabeled data. Given the CL training data, generative models can be trained to generate synthetic data to supplement the real data. Using both synthetic and real data for CL training has the potential to improve the quality of learned representations. However, synthetic data usually has lower quality than real data, and using synthetic data may not improve CL compared with using real data. To tackle this problem, we propose a data generation framework with two methods to improve CL training by joint sample generation and contrastive learning. The first approach generates hard samples for the main model. The generator is jointly learned with the main model to dynamically customize hard samples based on the training state of the main model. Besides, a pair of data generators are proposed to generate similar but distinct samples as positive pairs. In joint learning, the hardness of a positive pair is progressively increased by decreasing their similarity. Experimental results on multiple datasets show superior accuracy and data efficiency of the proposed data generation methods applied to CL. For example, about 4.0%, 3.5%, and 2.6% accuracy improvements for linear classification are observed on ImageNet-100, CIFAR-100, and CIFAR-10, respectively. Besides, up to 2× data efficiency for linear classification and up to 5× data efficiency for transfer learning are achieved.
Sentiment Analysis of Digital Television Migration on Twitter Using Naïve Bayes Multinomial Comparison, Support Vector Machines, and Logistic Regression Algorithms

The Ministry of Communication and Information Technology (KEMENKOMINFO) has announced to the publics in Indonesia regarding the termination of analog television broadcasts or called analog switch-off, which requires the public to migrate from analog television to digital television. Regarding the process of stopping analog broadcasts this raises pros and cons by the people in Indonesia. Many people give their respective opinions through social media, especially on Twitter. A collection of pros and cons data from the public can be collected and used as research of sentiment analysis. This research will focus on comparing three classification algorithms, which is called Multinomial Naïve Bayes, Support Vector Machines, and Logistic Regression using the same dataset and the same method called Lexicon Based. The results showed that the highest accuracy is Support Vector Machines with the accuracy is 94.00%, Logistic Regression with the accuracy is 90.00%, and Multinomial Naïve Bayes with the accuracy is 88.00%.
Progressive Few-Shot Adaptation of Generative Model with Align-Free Spatial Correlation

In few-shot generative model adaptation, the model for target domain is prone to the mode-collapse. Recent studies attempted to mitigate the problem by matching the relationship among samples generated from the same latent codes in source and target domains. The objective is further extended to image patch-level to transfer the spatial correlation within an instance. However, the patch-level approach assumes the consistency of spatial structure between source and target domains. For example, the positions of eyes in two domains are almost identical. Thus, it can bring visual artifacts if source and target domain images are not nicely aligned. In this paper, we propose a few-shot generative model adaptation method free from such assumption, based on a motivation that generative models are progressively adapting from the source domain to the target domain. Such progressive changes allow us to identify semantically coherent image regions between instances generated by models at a neighboring training iteration to consider the spatial correlation. We also propose an importance-based patch selection strategy to reduce the complexity of patch-level correlation matching. Our method shows the state-of-the-art few-shot domain adaptation performance in the qualitative and quantitative evaluations.
Sistem Deteksi Lampu Lalu Lintas Sebagai Asisten Pengemudi Menggunakan Convolutional Neural Network

Accident cases in Indonesia are increasing along with the increase in the number of motorized vehicles. From 2016 to 2017, speed limit violations increased by 96.20% and violations of road markings or signs also increased by 5.54%. Intelligent transportation system is one solution to reduce the number of accidents. Currently Driver Assistance Systems (DAS) are being developed in the automotive world. The purpose of this research is to design a watershed based on three input parameters for determining recommended actions, namely: 1) distance to the vehicle behind; 2) vehicle speed; and 3) traffic light status with recommendation action using fuzzy rule base. Lidar sensor for distance detection and GPS for monitoring vehicle speed. The YOLOv4 Algorithm method is used to detect traffic lights. The results of this study, the accuracy of sign color recognition is 92.831% with a detection speed of up to 8.94 FPS. The most stable reading distance is between 1 – 8-meters with a light intensity of 10 – 3200 lux and a tilt angle of up to 90 degrees. There is a drop in processing speed of up to 1.5 FPS during system integration. This DAS is effective enough to be applied to two-wheeled and fourwheeled motorized vehicles.
DENet: Disentangled Embedding Network for Visible Watermark Removal

Adding visible watermark into image is a common copyright protection method of medias. Meanwhile, public research on watermark removal can be utilized as an adversarial technology to help the further development of watermarking. Existing watermark removal methods mainly adopt multi-task learning networks, which locate the watermark and restore the background simultaneously. However, these approaches view the task as an image-to-image reconstruction problem, where they only impose supervision after the final output, making the high-level semantic features shared between different tasks. To this end, inspired by the two-stage coarse-refinement network, we propose a novel contrastive learning mechanism to disentangle the high-level embedding semantic information of the images and watermarks, driving the respective network branch more oriented. Specifically, the proposed mechanism is leveraged for watermark image decomposition, which aims to decouple the clean image and watermark hints in the high-level embedding space. This can guarantee the learning representation of the restored image enjoy more task-specific cues. In addition, we introduce a self-attention-based enhancement module, which promotes the network's ability to capture semantic information among different regions, leading to further improvement on the contrastive learning mechanism. To validate the effectiveness of our proposed method, extensive experiments are conducted on different challenging benchmarks. Experimental evaluations show that our approach can achieve state-of-the-art performance and yield high-quality images. The code is available at: https://github.com/lianchengmingjue/DENet.
Learning Event-Relevant Factors for Video Anomaly Detection

Most video anomaly detection methods discriminate events that deviate from normal patterns as anomalies. However, these methods are prone to interferences from event-irrelevant factors, such as background textures and object scale variations, incurring an increased false detection rate. In this paper, we propose to explicitly learn event-relevant factors to eliminate the interferences from event-irrelevant factors on anomaly predictions. To this end, we introduce a causal generative model to separate the event-relevant factors and event-irrelevant ones in videos, and learn the prototypes of event-relevant factors in a memory augmentation module. We design a causal objective function to optimize the causal generative model and develop a counterfactual learning strategy to guide anomaly predictions, which increases the influence of the event-relevant factors. The extensive experiments show the effectiveness of our method for video anomaly detection.
Efficient Image Captioning for Edge Devices

Recent years have witnessed the rapid progress of image captioning. However, the demands for large memory storage and heavy computational burden prevent these captioning models from being deployed on mobile devices. The main obstacles lie in the heavyweight visual feature extractors (i.e., object detectors) and complicated cross-modal fusion networks. To this end, we propose LightCap, a lightweight image captioner for resource-limited devices. The core design is built on the recent CLIP model for efficient image captioning. To be specific, on the one hand, we leverage the CLIP model to extract the compact grid features without relying on the time-consuming object detectors. On the other hand, we transfer the image-text retrieval design of CLIP to image captioning scenarios by devising a novel visual concept extractor and a cross-modal modulator. We further optimize the cross-modal fusion model and parallel prediction heads via sequential and ensemble distillations. With the carefully designed architecture, our model merely contains 40M parameters, saving the model size by more than 75% and the FLOPs by more than 98% in comparison with the current state-of-the-art methods. In spite of the low capacity, our model still exhibits state-of-the-art performance on prevalent datasets, e.g., 136.6 CIDEr on COCO Karpathy test split. Testing on the smartphone with only a single CPU, the proposed LightCap exhibits a fast inference speed of 188ms per image, which is ready for practical applications.
Implement Classification Approach for Software Defect Prediction

For any specific kind of application software is designed by considering its platforms related to hardware and operating system.Before initiating the development of software products, it is important to plan the process carefully.A detailed knowledge related to the actual requirement of the user and he processes to be performed when developing the software are must since the software is large in size.To generate the source code from a sequential model, the software engineering based abstract-present model is applied for generating the code of secondary phase, the code designed from one phase is given as input to design another code.For classifying the required and non-required code through which the next phase of code can be designed, the SVM classifier is applied.Python is used to implement the proposed model.Based on certain performance parameters, the results are evaluated in this research.
Administrative Data Automation of Civil Engineering Study Program Using Progressive Web Apps at Riau University

The Department of Civil Engineering, University of Riau has a Final Assignment Administration service. The Final Assignment Administration service procedure currently implemented is still done manually. Starting from the registration data collection for Proposal Seminars, Results Seminars, and Thesis Defence, data processing was carried out using Microsoft Excel. The amount of data that has been processed so that the processing is done too long. Likewise in Microsoft Excel where formula changes often occur so that the data is not readable. Therefore, the Thesis Administration System was built using the Progressive Web Apps (PWA) method. The system built facilitates administrative services in the Civil Engineering Study Program at the University of Riau. The built system can be accessed on various devices, using the add to home screen feature which speeds up installation, and can be accessed offline. Testing of this system uses the Black Box method, which is to prove that the functional system is running properly and correctly. The results of this test are 100% proven with features that run as expected.
Expert System for Diagnosing Areca Plant Diseases Using the Certainty Factor Method

Areca nut is a plant that is cultivated by the people of Papua as a means of livelihood. Areca nut has many benefits, starting from the fruit, leaves, roots, and the economic value of betel nut is also quite good. The problem often encountered by areca cultivators is the damage caused by areca plant diseases, causing losses both in terms of yield and control. In this study, the expert system for diagnosing areca plant diseases uses the Certainty Factor method, the essence of this method is to measure whether it is certain or uncertain in diagnosing disease based on the type of disease and disease symptoms found in areca plants, there are 8 types of areca plant diseases, namely spotting and yellowing, red leaf rust, stem base rot, fruit rot, shoot rot, root rot, flower and leaf fall and stunted plants. With the problems that occur, researchers will create an expert system that can help areca nut cultivators in diagnosing diseases in areca palm plants, it is hoped that this system can help areca cultivators by applying the Certainty Factor method which adopts expert knowledge into a computerized system so that the system can solve problems like an expert.
The Influence of Dimensions on the Complexity of Computing Decision Trees

A decision tree recursively splits a feature space \mathbb{R}^d and then assigns class labels based on the resulting partition. Decision trees have been part of the basic machine-learning toolkit for decades. A large body of work considers heuristic algorithms that compute a decision tree from training data, usually aiming to minimize in particular the size of the resulting tree. In contrast, little is known about the complexity of the underlying computational problem of computing a minimum-size tree for the given training data. We study this problem with respect to the number d of dimensions of the feature space \mathbb{R}^d, which contains n training examples. We show that it can be solved in O(n^(2d + 1)) time, but under reasonable complexity-theoretic assumptions it is not possible to achieve f(d) * n^o(d / log d) running time. The problem is solvable in (dR)^O(dR) * n^(1+o(1)) time, if there are exactly two classes and R is an upper bound on the number of tree leaves labeled with the first class.
Symbolic Resource of the Research and Pedagogical Community of Universities

Scientific and pedagogical stuff (SPS) are considered in the article as, on the one hand, one of the basic social communities of the university, on the other — as a scientific and professional community and scientific and educational community. SPS are characterized by the presence of a number of resources, of which one of the least studied is a symbolic resource. The article gives an interpretation of the symbolic resourcefulness of the SPS, shows its role and significance in the development of the entire resourcefulness of this social community. The purpose of the article is to analyze the symbolic resourcing of the SPS as a phenomenon of higher education. The symbolic resourcefulness of the SPS is the subject of scientific research in the proposed article.
Optimism in Face of a Context:Regret Guarantees for Stochastic Contextual MDP

We present regret minimization algorithms for stochastic contextual MDPs under minimum reachability assumption, using an access to an offline least square regression oracle. We analyze three different settings: where the dynamics is known, where the dynamics is unknown but independent of the context and the most challenging setting where the dynamics is unknown and context-dependent. For the latter, our algorithm obtains regret bound (up to poly-logarithmic factors) of order (H+1/pₘᵢₙ)H|S|³ᐟ²(|A|Tlog(max{|?|,|?|} /?))¹ᐟ² with probability 1−?, where ? and ? are finite and realizable function classes used to approximate the dynamics and rewards respectively, pₘᵢₙ is the minimum reachability parameter, S is the set of states, A the set of actions, H the horizon, and T the number of episodes. To our knowledge, our approach is the first optimistic approach applied to contextual MDPs with general function approximation (i.e., without additional knowledge regarding the function class, such as it being linear and etc.). We present a lower bound of ?((TH|S||A|ln|?| /ln|A| )¹ᐟ² ), on the expected regret which holds even in the case of known dynamics. Lastly, we discuss an extension of our results to CMDPs without minimum reachability, that obtains order of T³ᐟ⁴ regret.
Local-Global Defense against Unsupervised Adversarial Attacks on Graphs

Unsupervised pre-training algorithms for graph representation learning are vulnerable to adversarial attacks, such as first-order perturbations on graphs, which will have an impact on particular downstream applications. Designing an effective representation learning strategy against white-box attacks remains a crucial open topic. Prior research attempts to improve representation robustness by maximizing mutual information between the representation and the perturbed graph, which is sub-optimal because it does not adapt its defense techniques to the severity of the attack. To address this issue, we propose an unsupervised defense method that combines local and global defense to improve the robustness of representation. Note that we put forward the Perturbed Edges Harmfulness (PEH) metric to determine the riskiness of the attack. Thus, when the edges are attacked, the model can automatically identify the risk of attack. We present a method of attention-based protection against high-risk attacks that penalizes attention coefficients of perturbed edges to encoders. Extensive experiments demonstrate that our strategies can enhance the robustness of representation against various adversarial attacks on three benchmark graphs.
The Use of Partial Least Square for Analysis of the Relationship of Family Support on Learning Outcomes and Achievements Through Learning Motivation

Abstract: This study aims to analyze the relationship of family support to learning outcomes and learning achievement through learning motivation so that parents can increase their support for children in the effort to increase their learning motivation. This study uses a quantitative approach with partial least squares (SEM-PLS) analysis. The results showed that the score of the original sample of family support to learning motivation was 0.655, the score of learning motivation to learning outcomes was 0.605, and the score of learning motivation to learning achievement was 0.487. Thus, there is a positive relationship between family support and learning motivation, learning motivation and learning outcomes, and learning motivation and learning achievement. Therefore, to improve learning outcomes, learning achievement and learning motivation of children, parents must provide optimal support in the learning process to improve children's learning motivation.
Basic Data Research for Exhibition Space Management in the Post-COVID Era

In the post-COVID era, people began to think about different ways to manage these exhibition spaces. To this end, we examined the current status of selected exhibition spaces at home and abroad and conducted a survey. The results of the study showed that offline exhibitions, the traditional way of exhibition space management, are preferred to online exhibitions because they are more realistic and easier to understand, and the audience can relate to and interact with them more easily. This suggests that non-face-to-face content is not a substitute for existing offline exhibition spaces, but a complement to them. However, in response to the ever-changing digital environment, exhibition spaces should seek ways to adapt to these changes, and this warrants further research in a number of areas.
MEID: Mixture-of-Experts with Internal Distillation for Long-Tailed Video Recognition

The long-tailed video recognition problem is especially challenging, as videos tend to be long and untrimmed, and each video may contain multiple classes, causing frame-level class imbalance. The previous method tackles the long-tailed video recognition only through frame-level sampling for class re-balance without distinguishing the frame-level feature representation between head and tail classes. To improve the frame-level feature representation of tail classes, we modulate the frame-level features with an auxiliary distillation loss to reduce the distribution distance between head and tail classes. Moreover, we design a mixture-of-experts framework with two different expert designs, i.e., the first expert with an attention-based classification network handling the original long-tailed distribution, and the second expert dealing with the re-balanced distribution from class-balanced sampling. Notably, in the second expert, we specifically focus on the frames unsolved by the first expert through designing a complementary frame selection module, which inherits the attention weights from the first expert and selects frames with low attention weights, and we also enhance the motion feature representation for these selected frames. To highlight the multi-label challenge in long-tailed video recognition, we create two additional benchmarks based on Charades and CharadesEgo videos with the multi-label property, called CharadesLT and CharadesEgoLT. Extensive experiments are conducted on the existing long-tailed video benchmark VideoLT and the two new benchmarks to verify the effectiveness of our proposed method with state-of-the-art performance. The code and proposed benchmarks are released at https://github.com/VisionLanguageLab/MEID.
Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering

While a growing body of literature has been studying new Graph Neural Networks (GNNs) that work on both homophilic and heterophilic graphs, little has been done on adapting classical GNNs to less-homophilic graphs. Although the ability to handle less-homophilic graphs is restricted, classical GNNs still stand out in several nice properties such as efficiency, simplicity, and explainability. In this work, we propose a novel graph restructuring method that can be integrated into any type of GNNs, including classical GNNs, to leverage the benefits of existing GNNs while alleviating their limitations. Our contribution is threefold: a) learning the weight of pseudo-eigenvectors for an adaptive spectral clustering that aligns well with known node labels, b) proposing a new density-aware homophilic metric that is robust to label imbalance, and c) reconstructing the adjacency matrix based on the result of adaptive spectral clustering to maximize the homophilic scores. The experimental results show that our graph restructuring method can significantly boost the performance of six classical GNNs by an average of 25% on less-homophilic graphs. The boosted performance is comparable to state-of-the-art methods.