Reviewerly/exHarmony huggingface dataset:
Application of Dijkstra's Algorithm to Determine the Shortest Route from City Center to Medan City Tourist Attractions

Tourist attractions are very interesting things to visit in an area that we are living. It is no exception when visiting the city of Medan, the tourists will visit interesting tourist spots in the city of Medan. To optimize time so that you can visit all tourist attractions in Medan City, you need to map locations so that you can create the shortest route that can be used to take all the tourist sites you want to visit in Medan City. The shortest route of a trip will shorten the travel time. Likewise in terms of seeking experts. When requesting a route from one point (start point) to another location (destination point), usually the result that comes out is the "shortest path" from the starting point to the destination point. The shortest path is the problem of finding a path between two or more vertices in a minimum weighted graph. To simplify solving the shortest path problem, a search algorithm is needed. Dijkstra's algorithm solves the problem of finding the shortest path between two vertices in a weighted graph with the smallest total number, by finding the shortest distance between the initial vertex and other vertices, so that the path formed from the initial vertex to the destination vertex has the smallest total weight. In this study, Dijkstra's algorithm looks for the shortest path based on the smallest weight from one point to another, so that it can help provide a choice of paths. Based on the trials of Dijkstra's algorithm, it has the ability to find the shortest path, because in this algorithm each graph is selected an edge with a minimum weight that connects the selected vertices with other unselected vertices.
Application of K-Means Algorithm in Clustering Model for Learning Management System Usage Evaluation

The use of a learning management system (LMS) is one of the media that can be used to disseminate lecturer materials to students. Materials that can be uploaded on the LMS can be in the form of lecture materials in the form of files, videos, or questions. The effectiveness of LMS can be evaluated by looking at activities in using LMS. The effectiveness of using LMS can be seen from the log. Log results from LMS can be evaluated in various ways and one way is to use data mining clustering models. The clustering model can be used to create student groupings and the clustering results can be labeled in the form of categories, such as very good, good, and bad categories. This labeling depends on the clustering results that will be processed in the modeling. The research method uses CRISP DM which consists of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The beginning of the research process is carried out by taking log data in the Moodle LMS. The clustering model in this research will use the K-Means algorithm and the evaluation of clustering results will be evaluated for performance using the Davies-Bouldin method. Implementation of data mining processing using Rapid Miner application. The datasheet used is a datasheet taken from the LMS log of the Computer Programming course in the Mechanical Engineering study program - AKPRIND Institute of Science &amp; Technology Yogyakarta odd semester of the 2021/2022 and 2022/2023 academic years. The results of the study resulted in the best clustering based on the Davies Bouldin method of 2. The clustering results, cluster 0 consists of 28 data named the category of frequent access to LMS and cluster 1 consists of 54 with the category of not frequent access to LMS.
Beat the Bait: A Case Study on Phishing Attack

Every year, cybercriminals become savvier with their phishing attacks and have tried-and-tested methods to deceive and steal from innocent victims.With this, this case study aims to remind all Internet users, especially people who work from home, about the posing threats when browsing or clicking suspicious links, to give warning to those who work in their own homes about the posing threat of keyloggers, and to provide sufficient knowledge to those who confidently work in their home about the threats of phishing.The researchers conducted a survey to its target users through the zoom meeting application.Purpose of the research were discussed in the zoom meeting as well.Research results show that the work from home employees understands the importance of knowing phishing attacks and keyloggers.It is crucial to do a security check-up of personal computers occasionally.Initially, in the researcher's pre-test, only 35% of the respondents strongly agreed that understanding phishing is necessary.However, after demonstrating how keyloggers work and how it may impact their systems, the initial 35% rose to 60%.Only 10% of the respondents think knowing phishing and keyloggers will not make their pc safer nor impact their work from home jobs.
Effectiveness of virtual university education strategies in times of covid-19

The study made it possible to measure the effectiveness of virtual university education strategies in times of Covid-19. Methodologically, a quantitative approach was used, whose design was non-experimental, of a descriptive type, which used the survey as a technique and the questionnaire as an instrument. The population was made up of higher-level students from the universities of the Piura Region in the year 2022. The findings conclude that: (1). With training for teachers, administrators and students, the use of technology becomes a competitive advantage. (2). The teacher plays a key role in strengthening virtual education, promoting teamwork, flexibility, and demand. (3). It is in the hands of the teacher to provide induction support to ensure a healthy academic environment. (4). Under the perception of the student, virtual education does comply with consolidating the competencies and objectives of the course. Finally, 56% of the students who participated in this study present a medium level of satisfaction with virtual university education and 44% a high or high level. These results ratify the good performance and the optimal level of adaptation that this group of universities located in the northern part of Peru have been incorporating.
A Blockchain-Based Shared Bus Service Scheduling and Management System

With the continuous development of urbanization, it has become an important issue to effectively alleviate urban road traffic congestion and improve traffic efficiency. By combining blockchain technology and shared buses, this paper builds an intelligent traffic-service scheduling management system based on blockchain. The system effectively solves the core problems of shared buses, improves data security and privacy protection, realizes intelligent scheduling and route planning, and simplifies cross-organization cooperation and settlement processes. The research shows that the system can reduce the distance and number of buses, and improve the service quality and operation efficiency while ensuring the waiting time of passengers. The results of this paper verify the feasibility and advantages of the system, bring innovation and improvement to the field of traffic management, and promote the sustainable development of urban intelligent traffic management system. Future research could further explore the application of blockchain technology in traffic management to achieve more intelligent and sustainable urban traffic management.
Architecture of the Virtual World

The development of artificial intelligence-based virtual reality technology offers the potential of the digital world as an alternative activity space for humans. The virtual world is expected to complement the shortcomings of the natural world and meet human needs that cannot or are difficult to realize in the real world. The real world is formed by physical matter, while the virtual world comprises bits that cause a considerable difference between the two. This study aims to recognize the positive and negative potential due to differences between the real and virtual worlds concerning architecture. The goal is to answer how quickly the architecture of the virtual world can be put into practical use and dominate human life. The method used is the exploration of libraries and the internet, which are reviewed qualitatively. The results show that the idea of humans still dominates the world of virtual architecture as its creators. However, artificial intelligence can give new ideas beyond what humans think. Therefore, the virtual architectural world gives both positive and negative potential concerning differences in character from the real architectural world.
Sliding Window Sum Algorithms for Deep Neural Networks

Sliding window sums are widely used for string indexing, hashing and time series analysis. We have developed a family of the generic vectorized sliding sum algorithms that provide speedup of O(P/w) for window size w and number of processors P. For a sum with a commutative operator the speedup is improved to O(P/log(w)). Even more important, our algorithms exhibit efficient memory access patterns. In this paper we study the application of sliding sum algorithms to the training and inference of Deep Neural Networks. We demonstrate how both pooling and convolution primitives could be expressed as sliding sums and evaluated by the compute kernels with a shared structure. We show that the sliding sum convolution kernels are more efficient than the commonly used GEMM kernels on CPUs and could even outperform their GPU counterparts.
Quantum Search Approaches to Sampling-Based Motion Planning

In this paper, we present a novel formulation of traditional sampling-based motion planners as database-oracle structures that can be solved via quantum search algorithms.We consider two complementary scenarios: for simpler sparse environments, we formulate the Quantum Full Path Search Algorithm (q-FPS), which creates a superposition of full random path solutions, manipulates probability amplitudes with Quantum Amplitude Amplification (QAA), and quantum measures a single obstacle free full path solution.For dense unstructured environments, we formulate the Quantum Rapidly Exploring Random Tree algorithm, q-RRT, that creates quantum superpositions of possible parent-child connections, manipulates probability amplitudes with QAA, and quantum measures a single reachable state, which is added to a tree.As performance depends on the number of oracle calls and the probability of measuring good quantum states, we quantify how these errors factor into the probabilistic completeness properties of the algorithm.We then numerically estimate the expected number of database solutions to provide an approximation of the optimal number of oracle calls in the algorithm.We compare the q-RRT algorithm with a classical implementation and verify quadratic run-time speedup in the largest connected component of a 2D dense random lattice.We conclude by evaluating a proposed approach to limit the expected number of database solutions and thus limit the optimal number of oracle calls to a given number.
A Complex-Former Tracker With Dynamic Polar Spatio-Temporal Encoding

Recently, the excellent performance of transformer has attracted the attention of the visual community. Visual transformer models usually reshape images into sequence format and encode them sequentially. However, it is difficult to explicitly represent the relative relationship in distance and direction of visual data with typical 2-D spatial structures. Also, the temporal motion properties of consecutive frames are hardly exploited when it comes to dynamic video tasks like tracking. Therefore, we propose a novel dynamic polar spatio-temporal encoding for video scenes. We use spiral functions in polar space to fully exploit the spatial dependences of distance and direction in real scenes. We then design a dynamic relative encoding mode for continuous frames to capture the continuous spatio-temporal motion characteristics among video frames. Finally, we construct a complex-former framework with the proposed encoding applied to video-tracking tasks, where the complex fusion mode (CFM) realizes the effective fusion of scenes and positions for consecutive frames. The theoretical analysis demonstrates the feasibility and effectiveness of our proposed method. The experimental results on multiple datasets validate that our method can improve tracker performance in various video scenarios.
How to Best Apply Deep Neural Networks in Geosciences: Towards Optimal “Averaging” in Dropout Training

The main objectives of geosciences is to find the current state of the Earth – i.e., solve the corresponding inverse problems – and to use this knowledge for predicting the future events, such as earthquakes and volcanic eruptions. In both inverse and prediction problems, often, machine learning techniques are very efficient, and at present, the most efficient machine learning technique is deep neural training. To speed up this training, the current deep learning algorithms use dropout techniques: they train several sub-networks on different portions of data, and then “average” the results. A natural idea is to use arithmetic mean for this “averaging”, but empirically, geometric mean works much better. In this paper, we provide a theoretical explanation for the empirical efficiency of selecting geometric mean as the “averaging” in dropout training.
Nature-Inspired Algorithms in Optimization: Introduction, Hybridization, and Insights

Many problems in science and engineering are optimization problems, which may require sophisticated optimization techniques to solve. Nature-inspired algorithms are a class of metaheuristic algorithms for optimization, and some algorithms or variants are often developed by hybridization. Benchmarking is also important in evaluating the performance of optimization algorithms. This chapter focuses on the overview of optimization, nature-inspired algorithms, and the role of hybridization. We will also highlight some issues with hybridization of algorithms.
Accelerating Aggregation Queries on Unstructured Streams of Data

Analysts and scientists are interested in querying streams of video, audio, and text to extract quantitative insights. For example, an urban planner may wish to measure congestion by querying the live feed from a traffic camera. Prior work has used deep neural networks (DNNs) to answer such queries in the batch setting. However, much of this work is not suited for the streaming setting because it requires access to the entire dataset before a query can be submitted or is specific to video. Thus, to the best of our knowledge, no prior work addresses the problem of efficiently answering queries over multiple modalities of streams. In this work we propose InQuest, a system for accelerating aggregation queries on unstructured streams of data with statistical guarantees on query accuracy. InQuest leverages inexpensive approximation models ("proxies") and sampling techniques to limit the execution of an expensive high-precision model (an "oracle") to a subset of the stream. It then uses the oracle predictions to compute an approximate query answer in real-time. We theoretically analyzed InQuest and show that the expected error of its query estimates converges on stationary streams at a rate inversely proportional to the oracle budget. We evaluated our algorithm on six real-world video and text datasets and show that InQuest achieves the same root mean squared error (RMSE) as two streaming baselines with up to 5.0x fewer oracle invocations. We further show that InQuest can achieve up to 1.9x lower RMSE at a fixed number of oracle invocations than a state-of-the-art batch setting algorithm.
K-Nearest Neighbors Method for Recommendation System in Bangkalan’s Tourism

The more tourist objects are in an area, the more challenging it is for local governments to increase the selling value of these attractions. The government always strives to develop tourist attraction areas by prioritizing the beauty of tourist attractions. However, visitors often have difficulty in determining tourist objects that match their criteria because of the many choices. The research developed a tourist attraction recommendation system for visitors by applying machine learning techniques. The machine learning technique used was the K-Nearest Neighbor (KNN) method. Several trials were conducted with a dataset of 315 records, consisting of 11 attributes and 21 tourist attractions. Based on the dataset, the preprocessing stage was previously carried out to improve the data format by selecting data where the data were separated based on existing criteria, then calculating the closest distance and determining the value of k in the KNN method. The results are divided into five folds for each classification method. The highest system accuracy obtained at KNN is 78% at k=1. It shows that the KNN method can provide recommendations for three tourist attraction classes in Bangkalan. Applying the KNN method in the recommendation system determines several alternative tourist objects that tourists can visit according to their criteria in natural, cultural, and religious tourist objects.
Bi-directional Feature Fusion Generative Adversarial Network for Ultra-high Resolution Pathological Image Virtual Re-staining

The cost of pathological examination makes virtual restaining of pathological images meaningful. However, due to the ultra-high resolution of pathological images, traditional virtual restaining methods have to divide a WSI image into patches for model training and inference. Such a limitation leads to the lack of global information, resulting in observable differences in color, brightness and contrast when the re-stained patches are merged to generate an image of larger size. We summarize this issue as the square effect. Some existing methods try to solve this issue through overlapping between patches or simple postprocessing. But the former one is not that effective, while the latter one requires carefully tuning. In order to eliminate the square effect, we design a bidirectional feature fusion generative adversarial network (BFF-GAN) with a global branch and a local branch. It learns the inter-patch connections through the fusion of global and local features plus patch-wise attention. We perform experiments on both the private dataset RCC and the public dataset ANHIR. The results show that our model achieves competitive performance and is able to generate extremely real images that are deceptive even for experienced pathologists, which means it is of great clinical significance.
OpenMix: Exploring Outlier Samples for Misclassification Detection

Reliable confidence estimation for deep neural classifiers is a challenging yet fundamental requirement in high-stakes applications. Unfortunately, modern deep neural networks are often overconfident for their erroneous predictions. In this work, we exploit the easily available outlier samples, i.e., unlabeled samples coming from non-target classes, for helping detect misclassification errors. Particularly, we find that the well-known Outlier Exposure, which is powerful in detecting out-of-distribution (OOD) samples from unknown classes, does not provide any gain in identifying misclassification errors. Based on these observations, we propose a novel method called OpenMix, which incorporates open-world knowledge by learning to reject uncertain pseudo-samples generated via outlier transformation. OpenMix significantly improves confidence reliability under various scenarios, establishing a strong and unified framework for detecting both misclassified samples from known classes and OOD samples from unknown classes. The code is publicly available at https://github.com/Impression2805/OpenMix.
Domain Expansion of Image Generators

Can one inject new concepts into an already trained generative model, while respecting its existing structure and knowledge? We propose a new task - domain expansion - to address this. Given a pretrained generator and novel (but related) domains, we expand the generator to jointly model all domains, old and new, harmoniously. First, we note the generator contains a meaningful, pretrained latent space. Is it possible to minimally perturb this hard-earned representation, while maximally representing the new domains? Interestingly, we find that the latent space offers unused, “dormant” directions, which do not affect the output. This provides an opportunity: By “repurposing” these directions, we can represent new domains without perturbing the original representation. In fact, we find that pretrained generators have the capacity to add several- even hundreds - of new domains! Using our expansion method, one “expanded” model can supersede numerous domain-specific models, without expanding the model size. Additionally, a single expanded generator natively supports smooth transitions between domains, as well as composition of domains. Code and project page available here.
DivClust: Controlling Diversity in Deep Clustering

Clustering has been a major research topic in the field of machine learning, one to which Deep Learning has recently been applied with significant success. However, an aspect of clustering that is not addressed by existing deep clustering methods, is that of efficiently producing multiple, diverse partitionings for a given dataset. This is particularly important, as a diverse set of base clusterings are necessary for consensus clustering, which has been found to produce better and more robust results than relying on a single clustering. To address this gap, we propose Div-Clust, a diversity controlling loss that can be incorporated into existing deep clustering frameworks to produce multiple clusterings with the desired degree of diversity. We conduct experiments with multiple datasets and deep clustering frameworks and show that: a) our method effectively controls diversity across frameworks and datasets with very small additional computational cost, b) the sets of clusterings learned by DivClust include solutions that significantly outperform single-clustering baselines, and c) using an off-the-shelf consensus clustering algorithm, DivClust produces consensus clustering solutions that consistently outperform single-clustering baselines, effectively improving the performance of the base deep clustering framework. Code is available at https://github.com/ManiadisG/DivClust.
Make Landscape Flatter in Differentially Private Federated Learning

To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), clientlevel Differentially Private FL (DPFL) is the de-facto standard for privacy protection by clipping local updates and adding random noise. However, existing DPFL methods tend to make a sharper loss landscape and have poorer weight perturbation robustness, resulting in severe performance degradation. To alleviate these issues, we propose a novel DPFL algorithm named DP-FedSAM, which leverages gradient perturbation to mitigate the negative impact of DP. Specifically, DP-FedSAM integrates Sharpness Aware Minimization (SAM) optimizer to generate local flatness models with better stability and weight perturbation robustness, which results in the small norm of local updates and robustness to DP noise, thereby improving the performance. From the theoretical perspective, we analyze in detail how DP-FedSAM mitigates the performance degradation induced by DP. Meanwhile, we give rigorous privacy guarantees with Rényi DP and present the sensitivity analysis of local updates. At last, we empirically confirm that our algorithm achieves state-of-the-art (SOTA) performance compared with existing SOTA baselines in DPFL.