Reviewerly/exHarmony huggingface dataset:
Pap Smear Medical Image Classification Using Deep Learning: A Systematic Review

Cervical cancer has been known as one of the most prevalent medical disorders globally and a leading cause of death. Early detection, particularly through Pap tests, plays a vital role in its prevention. Previous studies have leveraged machine learning and deep learning techniques to classify the medical images obtained from Pap tests. In this study, a Systematic Literature Review methodology was used to examine 15 relevant papers that have been filtered from queries to Google Scholar which have gone through 4 stages of filtering that include: identification, screening, eligibility, and inclusion. This study addresses two research questions regarding the datasets and deep learning techniques for classifying pap smear images in recent years. The performance of the models was analyzed and potential areas for improvements are suggested. The findings of this study reveal that the Herlev University Hospital and SIPaKMed datasets are the most used. The methodologies used by researchers range from machine learning techniques, transfer learning using Convolutional Neural Networks, and utilize state-of-the-art models with novel optimizing methodology. While there are exciting opportunities in the field, challenges include model generalization and interpretability.
Research Service Issues of Chiang Mai Chinese Restaurants to Chinese Tourists Based on Text Analysis and Sentiment Analysis

This study aims to identify specific service issues in Chinese restaurants serving Chinese tourists in Chiang Mai, Thailand. The study first uses python to mine the reviews of Chiang Mai Chinese restaurants on Dianping.com, and then uses sentiment analysis software (weiciyun) to conduct a comprehensive analysis of the top 10 and bottom 10 reviews of Chiang Mai Chinese restaurants. Reviews are coded in terms of responsiveness, reliability, assurance, empathy and tangibility against relevant service quality criteria. The analysis showed that the most common service issues mentioned in reviews included Environmental and hygiene issues, Food quality issues, and Chinese service issues. The study recommends that Chinese restaurants in Chiang Mai improve these aspects toincrease Chinese tourists' satisfaction. The results of this study can also provide a reference for service improvement for Chinese restaurants in Thailand and other Southeast Asian tourist countries when serving Chinese tourists.
Optimizing the Performance of Sentiment Analysis on Social Media Data Through Feature Selection in Preprocessing Techniques

The digital era directly builds user needs in communicating using social media, the data generated by social media has a large volume so it needs to be analyzed with a preprocessing process. The problems that exist in social media data contain a lot of repeated words with non-standard sentence structures and still contain lots of irregular punctuation making it difficult to determine which features are considered important in a sentence to carry out a sentiment analysis process in identifying positive, negative, and neutral sentences. So it is necessary to select features using preprocessing techniques using Recursive Feature Elimination (RFE). This study produces a comparison that shows that the selection of features and combinations in using features results in the accuracy of each algorithm increasing when compared to using one feature. The results showed that the CNN algorithm has an accuracy value of 0.95, the LSTM algorithm is 0.94 and the RNN algorithm is 0.93. when compared to the use of one feature, it increases by 0.09 for the CNN algorithm, increases by 0.07 for the LSTM algorithm, and increases by 0.05 for the RNN algorithm.
Efficiency Measurement of Energy Yield from Solar Photovoltaic Cell with Embedded System of Alternative Clamp Meter

This paper presents a data analysis of energy production by photovoltaic systems at Prince of Songkla University, Surat Thani Campus, with a focus on the installation of a solar cell system on the campus roof. The energy generated by this system is transmitted to the main electrical control cabinet of the building. However, in case of a malfunction in the solar cell set, inspecting and identifying the cause of the abnormality can be challenging and time-consuming. Therefore, the use of current measuring devices is necessary to assess the operation of each inverter set in the photovoltaic system, facilitating the identification and prediction of abnormalities within the solar cell set. To achieve this, we conducted a linear regression analysis employing a machine learning model. Our findings indicate that the Mean Absolute Error (MAE) model is the most suitable machine learning model for determining the efficiency of the photovoltaic system in terms of current sensing, with analysis results of Phase A = 0.8414, Phase B = 1.0128, and Phase C = 0.8129. Additionally, for voltage sensing, the most appropriate model is the Mean Absolute Percentage Error (MAPE), with an analysis result of Phase A = 0.4857, Phase B = 0.5057, and Phase C = 0.4571. These results provide valuable insights for maintenance purposes and contribute to enhancing the overall functionality of the photovoltaic system.
Modified YOLO Model for Small Platform Application Using SimRepCSP Module with Case Study: Objects Detection Model on GlobalWheat2020 Dataset

In this research, the optional backbone for YOLO model, called SimRepCSP (Simple Re-parameterization Cross Stage Partial module) was proposed, the model aims to address the goals of reducing training and application costs while enhancing model performance on specific dataset. SimRepCSP is constructed by combining Convolution modules and Re-parameterization Convolution (RepConv) modules, similar to YOLOv7, with the Cross Stage Partial (CSP) network serving as the connection between modules. By incorporating SimRepCSP into YOLOv8 as an alternative backbone, the research aims to achieve improvements in both training efficiency and model performance compared to the original backbone. The experiments were conducted using the GlobalWheat2020 dataset, and the results demonstrate a reduction in training and application costs, as well as higher performance metrics compared to YOLOv8 with the original backbone.
Handling Various Conditions in a Web Service Client's Method by Using the Visitor Design Pattern

This research demonstrates how the visitor design pattern can support software developers to professionally handle information retrieval provided in different formats from different web services. By designing diagrams based on the basic object-oriented programming and the visitor design patterns, using the UML, two diagrams are used as a guide for developing two Android applications for showing the cryptocurrency data retrieved from different web services. Such data has various amounts of information and different names, although they are in the same format, such as JSON. Memory usage is detected when both applications are being used by the Android emulator and the physical mobile device. The result shows that the visitor application consumes much more memory than the object-oriented application in the view of all classes. But both applications consume almost the same amount of memory in the view of project classes and on the physical mobile device. Therefore, the visitor design pattern does not affect memory consumption.
AXREL: Automated Extracting Registry and Event Logs for Windows Forensics

When a cyber incident occurs, digital forensic is then essential for investigating how hackers compromised the system or how malware functioned. In this paper, we focus on Windows forensics which is one important branch of digital forensics. Windows forensics can be performed using some existing investigation tools that are expensive and require training before using them, while the current number of well-trained staffs in the cybersecurity field is limited. Moreover, in the step of evidence analysis, Windows forensic investigators need to manually extract certain files such as Windows registry and Windows event logs, which is a repetitive and time-consuming task. Therefore, we propose AXREL as an automated Windows evidence extracting application to facilitate new Windows forensic investigators by providing a user-friendly GUI. Our application is developed by Python 3 on the Windows platform. It can automatically extract Windows registry and event logs, which are the primary sources of evidence for Windows forensics.
FedGTA: Topology-Aware Averaging for Federated Graph Learning

Federated Graph Learning (FGL) is a distributed machine learning paradigm that enables collaborative training on large-scale subgraphs across multiple local systems. Existing FGL studies fall into two categories: (i) FGL Optimization, which improves multi-client training in existing machine learning models; (ii) FGL Model, which enhances performance with complex local models and multi-client interactions. However, most FGL optimization strategies are designed specifically for the computer vision domain and ignore graph structure, presenting dissatisfied performance and slow convergence. Meanwhile, complex local model architectures in FGL Models studies lack scalability for handling large-scale subgraphs and have deployment limitations. To address these issues, we propose Federated Graph Topology-aware Aggregation (FedGTA), a personalized optimization strategy that optimizes through topology-aware local smoothing confidence and mixed neighbor features. During experiments, we deploy FedGTA in 12 multi-scale real-world datasets with the Louvain and Metis split. This allows us to evaluate the performance and robustness of FedGTA across a range of scenarios. Extensive experiments demonstrate that FedGTA achieves state-of-the-art performance while exhibiting high scalability and efficiency. The experiment includes ogbn-papers100M, the most representative large-scale graph database so that we can verify the applicability of our method to large-scale graph learning. To the best of our knowledge, our study is the first to bridge large-scale graph learning with FGL using this optimization strategy, contributing to the development of efficient and scalable FGL methods.
Flexible Resource Allocation for Relational Database-as-a-Service

Oversubscription is an essential cost management strategy for cloud database providers, and its importance is magnified by the emerging paradigm of serverless databases. In contrast to general purpose techniques used for oversubscription in hypervisors, operating systems and cluster managers, we develop techniques that leverage our understanding of how DBMSs use resources and how resource allocations impact database performance. Our techniques are designed to flexibly redistribute resources across database tenants at the node and cluster levels with low overhead. We have implemented our techniques in a commercial cloud database service: Azure SQL Database. Experiments using microbenchmarks, industry-standard benchmarks and real-world resource usage traces show that using our approach, it is possible to tightly control the impact on database performance even with a relatively high degree of oversubscription.
Solver-In-The-Loop Cluster Resource Management for Database-as-a-Service

In Database-as-a-Service (DBaaS) clusters, resource management is a complex optimization problem that assigns tenants to nodes, subject to various constraints and objectives. Tenants share resources within a node, however, their resource demands can change over time and exhibit high variance. As tenants may accumulate large state, moving them to a different node becomes disruptive, making intelligent placement decisions crucial to avoid service disruption. Placement decisions need to account for dynamic changes in tenant resource demands, different causes of service disruption, and various placement constraints, giving rise to a complex search space. In this paper, we show how to bring combinatorial solvers to bear on this problem, formulating the objective of minimizing service disruption as an optimization problem amenable to fast solutions. We implemented our approach in the Service Fabric cluster manager codebase. Experiments show significant reductions in constraint violations and tenant moves, compared to the previous state-of-the-art, including the unmodified Service Fabric cluster manager, as well as recent research on DBaaS tenant placement.
SparqLog: A System for Efficient Evaluation of SPARQL 1.1 Queries via Datalog

Over the past decade, Knowledge Graphs have received enormous interest both from industry and from academia. Research in this area has been driven, above all, by the Database (DB) community and the Semantic Web (SW) community. However, there still remains a certain divide between approaches coming from these two communities. For instance, while languages such as SQL or Datalog are widely used in the DB area, a different set of languages such as SPARQL and OWL is used in the SW area. Interoperability between such technologies is still a challenge. The goal of this work is to present a uniform and consistent framework meeting important requirements from both, the SW and DB field.
GraphOS: Towards Oblivious Graph Processing

We propose GraphOS, a system that allows a client that owns a graph database to outsource it to an untrusted server for storage and querying. It relies on doubly-oblivious primitives and trusted hardware to achieve a very strong privacy and efficiency notion which we call oblivious graph processing : the server learns nothing besides the number of graph vertexes and edges, and for each query its type and response size. At a technical level, GraphOS stores the graph on a doubly-oblivious data structure , so that all vertex/edge accesses are indistinguishable. For this purpose, we propose Omix++, a novel doubly-oblivious map that outperforms the previous state of the art by up to 34×, and may be of independent interest. Moreover, to avoid any leakage from CPU instruction-fetching during query evaluation, we propose algorithms for four fundamental graph queries (BFS/DFS traversal, minimum spanning tree, and single-source shortest paths) that have a fixed execution trace , i.e., the sequence of executed operations is independent of the input. By combining these techniques, we eliminate all information that a hardware adversary observing the memory access pattern within the protected enclave can infer. We benchmarked GraphOS against the best existing solution, based on oblivious relational DBMS (translating graph queries to relational operators). GraphOS is not only significantly more performant (by up to two orders of magnitude for our tested graphs) but it eliminates leakage related to the graph topology that is practically inherent when a relational DBMS is used unless all operations are "padded" to the worst case.
Doquet: Differentially Oblivious Range and Join Queries with Private Data Structures

Most cloud service providers offer limited data privacy guarantees, discouraging clients from using them for managing their sensitive data. Cloud providers may use servers with Trusted Execution Environments (TEEs) to protect outsourced data, while supporting remote querying. However, TEEs may leak access patterns and allow communication volume attacks, enabling an honest-but-curious cloud provider to learn sensitive information. Oblivious algorithms can be used to completely hide data access patterns, but their high overhead could render them impractical. To alleviate the latter, the notion of Differential Obliviousness (DO) has been recently proposed. DO applies differential privacy (DP) on access patterns while hiding the communication volume of intermediate and final results; it does so by trading some level of privacy for efficiency. We present Doquet: D ifferentially O blivious Range and Join Que ries with Private Data Struc t ures, a framework for DO outsourced database systems. Doquet is the first approach that supports private data structures, indices, selection, foreign key join, many-to-many join, and their composition select-join in a realistic TEE setting, even when the accesses to the private memory can be eavesdropped on by the adversary. We prove that the algorithms in Doquet satisfy differential obliviousness. Furthermore, we implemented Doquet and tested it on a machine having a second generation of Intel SGX (TEE); the results show that Doquet offers up to an order of magnitude speedup in comparison with other fully oblivious and differentially oblivious approaches.
VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building

We introduce VOCALExplore, a system designed to support users in building domain-specific models over video datasets. VOCALExplore supports interactive labeling sessions and trains models using user-supplied labels. VOCALExplore maximizes model quality by automatically deciding how to select samples based on observed skew in the collected labels. It also selects the optimal video representations to use when training models by casting feature selection as a rising bandit problem. Finally, VOCALExplore implements optimizations to achieve low latency without sacrificing model performance. We demonstrate that VOCALExplore achieves close to the best possible model quality given candidate acquisition functions and feature extractors, and it does so with low visible latency (~1 second per iteration) and no expensive preprocessing.
Generating Personas for Games with Multimodal Adversarial Imitation Learning

Reinforcement learning has been widely successful in producing agents capable of playing games at a human level. However, this requires complex reward engineering, and the agent’s resulting policy is often unpredictable. Going beyond reinforcement learning is necessary to model a wide range of human playstyles, which can be difficult to represent with a reward function. This paper presents a novel imitation learning approach to generate multiple persona policies for playtesting. Multimodal Generative Adversarial Imitation Learning (Multi-GAIL) uses an auxiliary input parameter to learn distinct personas using a single-agent model. MultiGAIL is based on generative adversarial imitation learning and uses multiple dis-criminators as reward models, inferring the environment reward by comparing the agent and distinct expert policies. The reward from each discriminator is weighted according to the auxiliary input. Our experimental analysis demonstrates the effectiveness of our technique in two environments with continuous and discrete action spaces.
Continuous Episodic Control

Non-parametric episodic memory can be used to quickly latch onto high-rewarded experience in reinforcement learning tasks. In contrast to parametric deep reinforcement learning approaches in which reward signals need to be back-propagated slowly, these methods only need to discover the solution once, and may then repeatedly solve the task. However, episodic control solutions are stored in discrete tables, and this approach has so far only been applied to discrete action space problems. Therefore, this paper introduces Continuous Episodic Control (CEC), a novel non-parametric episodic memory algorithm for sequential decision making in problems with a continuous action space. Results on several sparse-reward continuous control environments show that our proposed method learns faster than state-of-the-art model-free RL and memory-augmented RL algorithms, while maintaining good long-run performance as well. In short, CEC can be a fast approach for learning in continuous control tasks. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
path2level: Constraint-Based Level Generation from Paths

Player paths are an important consideration when procedurally generating levels. However, player paths are typically incorporated as part of an evaluation process or overlaid on level structure in training data. Here we present a constraint-based level generation approach that takes a path through the level as input and attempts to generate a level containing that path. We describe an interactive level generation tool where the generator creates levels based on paths the user draws. We characterize the types of paths and levels generated by the approach.
FrAG: A Framework for the Analysis of Games

FrAG is a Framework for the Analysis of Games, allowing us to harness game-playing AI techniques to assist in the reverse engineering of historical video games for which the binary form of the game is the only thing remaining. Moreover, FrAG allows us to perform this task at scale, and search for patterns of game development that might otherwise be overlooked. Game development is done by humans, of course; our interdisciplinary approach is not exclusively technical, and also involves the field of archaeology and its well-established theories of humans and technology. Here we present FrAG’s architecture along with our preliminary results using the framework on a test suite of Atari 2600 games.
Active Learning for Classifying 2D Grid-Based Level Completability

Determining the completability of levels generated by procedural generators such as machine learning models can be challenging, as it can involve the use of solver agents that often require a significant amount of time to analyze and solve levels. Active learning is not yet widely adopted in game evaluations, although it has been used successfully in natural language processing, image and speech recognition, and computer vision, where the availability of labeled data is limited or expensive. In this paper, we propose the use of active learning for learning level completability classification. Through an active learning approach, we train deep-learning models to classify the completability of generated levels for Super Mario Bros., Kid Icarus, and a Zelda-like game. We compare active learning for querying levels to label with completability against random queries. Our results show using an active learning approach to label levels results in better classifier performance with the same amount of labeled data.
Human Interactive Learning with Intrinsic Reward

Recently, deep reinforcement learning (RL) algorithms have been advanced, and especially, Importance Weighted Actor-Learner Architecture (IMPALA) outperformed human expert scores in some of the Atari-2600 games. However, in the Bowling of Atari games where there is a serious sparse reward problem, IMPALA has poor performance. The sparse reward problem, which arises from the requirement of a sequence of actions, is a significant challenge in reinforcement learning. To address this problem, we propose human interactive learning with intrinsic reward as a solution. Combining human interactive learning and intrinsic reward into an RL algorithm, we build a sequential action guiding system in the training agent. As a result of combining the feedback neural network and intrinsic reward, experimental results show efficient convergence to a higher score than the baseline algorithm in Bowling.