Reviewerly/exHarmony huggingface dataset:
Asynchronous Non–uniform Distributed Multi–target Tracking Filter Based on Asymmetric Alpha–divergence Consensus

With more and more extensive application of target tracking, distributed multi–target tracking (DMT) becomes an important research direction. However, the synchronization characteristics of some sensor networks can not be guaranteed due to communication delay, non–uniform sampling and so on. Therefore, a DMT algorithm for asynchronous non–uniform multi–sensor networks is studied in this paper. Firstly, by using the cardinalized probability hypothesis density (CPHD) based on continuous–discrete multi–target dynamic (CD) model (CD-CPHD), a CD–CPHD algorithm with multi–step birth process and time–triggered structure (TCD–CPHD) is proposed; Secondly, an asymmetric alpha–divergence (AAD) based adaptive computing structure of time–trigger index is designed to reduce the communication rate of TCD–CPHD; Thirdly, the fusion rule of AAD consensus is derived to construct the fusion process of the proposed algorithm; Finally, by combining the above structures and the fusion rule, the implementation process of the proposed algorithm is given. Theoretical analysis and exhaustive experimental analysis show the effectiveness of the proposed algorithm.
A Triplet Contrast Learning of Global and Local Representations for Unannotated Medical Images

Recently, self-supervised learning(SSL) has shown its great potential in representation learning and been applied to various computer vision tasks. With the success of SSL, which showed performance improvement in natural images, SSL research is actively being conducted in medical image analysis. In this paper, we present a triplet network for the medical image representation learning to learn robust patterns of medical images against global and local changes by comparing latent feature distance between positive and negative pairs with anchors. This approach does not require large batches or the asymmetry of the network. It has been experimentally shown that the proposed method can outperform ImageNet pretrained models and the state-of-the-art SSL methods.
Classification of normal and abnormal bladder CT images using support vector machine

Bladder cancer is the 10th most commonly diagnosed cancer worldwide, and the 13th in Indonesia, according to database providing global cancer statistics and estimates of incidence and mortality (GLOBOCAN 2020). The gold standard in the diagnosis of bladder cancer is computed tomography (CT). However, the CT modality produces multiple images and each image has a different size, shape, and location of bladder cancer. Therefore, to improve early diagnostic accuracy from the image directly for each patient, Computer-Aided Diagnosis (CAD) can be an assistant or a tool for radiologist reinforcement in classifying normal and abnormal images. In this study, the CAD system was developed using the K-Nearest Neighbor (KNN) segmentation method, feature extraction based on Gray Level Co-Occurrence Matrix (GLCM), and normal and abnormal image classification using the Support Vector Machine. The data used in this study are 150 bladder CT images from Dharmais National Cancer Hospital, consisting of 75 normal images and 75 abnormal images. 100 images are used as training data, and 50 images are used as testing data. The results of CAD system performance in this study are in the form of the accuracy of 91% for training data and 88% for testing data.
A Large-Scale Measurement and Optimization of Mobile Live Streaming Services

Mobile Live Streaming (MLS) services are one of the most popular types of mobile apps. They involve a (often amateur) user broadcasting content to a potentially large online audience via unreliable networks. Nevertheless, we still lack a deep understanding of MLS user behavior that is critical for optimizing MLS systems, despite some active measurements on viewer-side behavior. Using detailed logs obtained from a major MLS provider, this paper first conducts an in-depth measurement study of both viewer-side and broadcaster-side behavior. Key findings include large wasteful uploads, strong viewing locality, and traffic dominance of loyal viewers. Specifically, 33.3% of uploads go unwatched, and the viewership of broadcasters tends to be localized. Inspired by our findings, we propose EDGEOPT– a centralized control center for MLS services for optimizing both the first-mile and the last-mile transmission in MLS. Specifically, EDGEOPT reduces wasteful uploading by 71% through adaptive uploading and enhances the replay quality of popular video segments by 10% via highlights retransmission. EDGEOPT also uses a learning-based content pre-fetching scheme that boosts the viewing startup by 29.5% and offloads at most 80% of the viewing workload from the edge servers with peer-assisted delivery.
Application of Apriori Algorithm for CRM Improvement - Case Study from Montenegro

In this paper, a case study of market basket analysis is conducted, using the Apriori algorithm. This method constructs a set of association rules, aiming to find the groups of products that are often purchased together. The algorithm was applied to the real-life data set from a cosmetics retailer from Montenegro. The resulting rules can help decision-makers gain insight into interesting sets of products that appear in transactions together, which is of great importance for designing future marketing strategies. This approach, as well as several other data mining methods, are proved to be significant in knowledge discovery and the decision-making process for CRM improvement.
Cybersecurity Modelling for SCADA Systems: A Case Study

This paper describes a cybersecurity model for Supervisory Control and Data Acquisition system (SCADA) using techniques similar to those used in reliability systems modelling. Previously, cybersecurity events were considered a part of the reliability events of a cyber physical system [1] [2]. Our approach identifies and treats such events separately as unique class of events by itself. Our analyses shows that the hierarchical model described below has the potential for quantifying the cybersecurity posture of a SCADA system, which goes beyond the usual pass/fail metrics that are currently in use [3]. A range of Mean Time to Security Failure (MTTSF) values as shown in the sensitivity studies below can capture both peacetime and wartime cyber risk assessment of the system. While the Attack and Countermeasure Tree (ACT) constructed below could be taken as somewhat simplistic, more detailed security events can be readily introduced to the ACT tree to reflect a better depiction of a cyberattack. For example, the Common Processing Systems (CPS) systems themselves can be further resolved into constituent components that are vulnerable to cyberattacks. Separate models can also be developed for each of the individual failure events, i.e. confidentiality, integrity, and availability, instead of combining them into one failure event as done below. The methodology for computing the MTTSF metric can be extended to other similar cybersecurity metrics, such as those formulated by the Center for Internet Security (CIS) [3], e.g. mean time to restore to operational status, etc. Additional improvements to the model can be obtained with the incorporation of the repair and restore portion of the semi-Markov chain in Figure 3, which will likely require the use of more advance modeling packages.
Using Natural Language Processing for Aftermarket Text to Increase Accuracy and Efficiency

In aftermarket or field data, great strides are taken to analyze the numerical information within. However, the information that is contained in the text can be equally important and oftentimes takes a back seat to numerical data. There is a need to extract this valuable text data accurately and efficiently within the aftermarket systems. There are many challenges unique to text data which warrants a different approach. For example, repositories may have changed or evolved over the years, and they may contain a variety of structures. One may have drop down choices with text, others may have unstructured long field text, and within long field text you may find several ways to state something with the exact same meaning. When there is a large amount of data, interpreting text data can be lengthy and riddled with errors. To overcome this challenge for larger aftermarket data sets, a solution is to use NLP (Natural Language Processing).
NLP for Data Cleansing in DoD Maintenance Data: Case Study of Work Unit Code Correction for LAU-127

The objective of this paper is to develop a data-cleansing approach that leverages text data in Maintenance Action Forms (MAF) to improve the data quality of coded fields, such as Work Unit Code (WUC) for the Department of Defense (DoD) maintenance data. The poor data quality of DoD maintenance data is a critical issue, experienced by a wide range of organizations, that leads to poor predictive modeling, lack of transparency in performed maintenance, and adverse downstream effects due to incorrect reporting.
Banking Data Enrichment Pipeline

In the present day, there has been a rapid growth of fintech companies all around the globe, providing services like market data analytics, banking solutions, mobile banking, expense management, etc. Many such fin-tech companies rely majorly on banking data. However, bank data, like transactions and holdings data, contains very concise intricate string descriptions which are not machine readable. These string descriptions do not follow any standardized protocols making them harder to convert it into organized data. Hence we propose an intelligent data enrichment pipeline that employs deep-learning based BERT model to classify the transaction contract type and advanced algorithms like suffix tree and tools like regular expression to extract all the valuable information. The pipeline returns categorized and structured machine readable data objects which can form the backbone of all the fintech software. We were able to achieve contract type classification accuracy of 98.4% and were able to extract all the valuable data from the string description. In order to understand the working of the BERT based deep-learning model, we reverse engineered the model using attention networks. The results of the same are also presented in this paper.
Pedestrian Detection in Crowded Scenes Based on Cascade R-CNN

The occlusion in crowded scenes and the interference of similar objects in the background are one of the main reasons that lead to missed pedestrian detection. In response to this problem, an improved Cascade R-CNN pedestrian detection algorithm using dynamic regressors is proposed. Firstly, the offset of each sample is adjusted to a dynamic offset and normalization to strengthen the regression performance of each regressor. Then, based on the preliminary regression, a secondary detection module of occluded pedestrians is constructed to further enhance the discrimination between pedestrians and pedestrians. The proposed algorithm performed ablation experiments on the datasets Caltech, CityPersons and CrowdHuman, and the missed detection rates were reduced by 21.3%, 5.0% and 8.4% on the Heavy subset of the above three datasets, respectively. The experimental results show that the improved Cascade R-CNN algorithm is strongly robust to obstructed pedestrians.
Way - A Fast Front-end Generator Framework Based on Python Processing

This paper has provided a new methodology of designing web front-end frameworks. Way is designed as a convenient and efficient front-end developing framework. Way has combined the advanced characteristics of the convenient developing of JavaScript (JS) front-end frameworks and the strong support SEO of native HTML developing. The main idea of the design and implementation of Way is using the Python scripting as a monitor and interpreter, which make the developer focus more on the design idea rather than coding. One of the most significant characteristics of Way is that the developer can develop a native HTML project in a high efficiency of using a JS framework when using Way. The paper discusses and analyses the background, advantages and disadvantages of using a JS framework, and the relationships between JS frameworks and SEO. Subsequently, the paper analyses the basic design principle of Way and presented the significant implementation methods of Way. Followed by the discussions and analysis of the advantages and performances of Way. The expansibilities and developing trends of Way has been discussed in the end of the paper. As an open source framework in GitHub, Way is a new and useful design idea more than a practical web front-end desing and developing framework.
Metrics for machine learning evaluation methods in cloud monitoring systems

During the machine learning pipeline development, engineers need to validate the efficiency of the machine learning methods in order to assess the quality of the made forecast.
An Initial Comparison of Selected Agent-Based Simulation Tools in the Context of Industrial Health and Safety Management

Agent-based approaches are becoming more and more popular for the modelling and simulation of natural phenomena, behaviour-specific patterns as well as in the health and safety area. In this paper we aim to evaluate and compare selected agent-based software tools. A selection is necessary due to this field being a fast evolving one, which offers numerous software frameworks and tools. Since our focus lies in the health and safety area, the primary selection criterion was the applicability in the field of social sciences. Furthermore the modeling strength of the software tools was considered. Based on these selection criteria NetLogo, GAMA and Repast are used for an initial comparison, which involves technical details, modelling, simulation and data exchange. To compare the agent-based software tools in a experimental setup, we implemented Conway's Game of Life. In these experiments, NetLogo outperforms both GAMA and Repast Simphony ReLogo with regard to execution speed. We conclude that in our context, NetLogo and GAMA appear to be more suitable than Repast. The latter - although very versatile - requires a high amount a programming skills. GAMA's primary strength on the other hand is the heavy focus on GIS data involvement, while NetLogo is not only very intuitive in learning but offers also a large number of features.
Cluster-oriented virtual machine low latency consolidation algorithm

With the growing amount of data processed in the virtual environment, many researchers focus their efforts on optimizing the load distribution on data centers according to various criteria. In this article, we propose optimization at the network infrastructure load of the data center. The new heuristic algorithm, based on grouping virtual machines into clusters, was compared with heuristics based on a genetic algorithm. The performed measurements indicate that clustering-based heuristics, although data-dependent, shows promising characteristics with significantly lower computational complexity. The algorithm was tested on a rigorous number of instances, proving its general usability.
Modified Support Vector Machine using Giza Pyramids Construction Algorithm

Support vector machine (SVM) is a machine learning algorithm used for classification. SVM is commonly used in image, video, speech, and text recognition. The C value in SVM determines the degree of error for misclassification that the model will accept in the training set for the sake of generalization. One problem in the algorithm lies in finding the optimal value of this parameter. This problem affects the SVM model in achieving higher performance by chances of overfitting. In this paper, the Giza Pyramids Construction (GPC) Algorithm was proposed to find the optimal value of C. GPC finds an optimal C value by simulating how the workers were able to push stone blocks for the construction of the Giza Pyramid in the ancient times. The best position of the worker where it will be able to push the block most efficiently is calculated and was used as the C value for the SVM model. The proposed model was compared to the original SVM. Confusion matrix, F1-score, and accuracy were used to measure the performance of each model. The proposed model achieved a global accuracy of 97.27% while the original SVM achieved 94.54% in the Chars74K handwriting dataset. GPC-SVM has also achieved higher global accuracies on the MNIST dataset and the Chars74K natural images dataset compared to the original SVM. It was concluded that the proposed model achieved greater performance on all datasets compared to the original SVM in recognizing digits in the dataset.
Bayesian Opponent Exploitation by Inferring the Opponent’s Policy Selection Pattern

In a multi-agent competitive domain, the agent needs to anticipate the opponent’s behavior and select a suitable policy to exploit the opponent. In this work, based on the BPR (Bayesian Policy Reuse) framework, we further assume the opponent may determine its policy depending on its previous observation. To deal with opponents of this kind, we discuss three different approaches for the agent, including learning from scratch, reasoning from experience, and reasoning accompanied by learning. The “reasoning accompanied by learning” approach turns out to be the most favorable method, in which the agent executes an iterative process that alternates between “updating the belief of each pre-collected model” and “progressively learning the opponent’s policy selection pattern” based on the observed data. In our experiments, we simulate a simplified batter vs. pitcher game. The experimental results show that the “reasoning accompanied by learning” approach does receive a larger averaged utility value than the learn-from-scratch approach and the reason-from-experience approach.
Unsupervised Contrastive Learning for Power Equipment Image Recognition

Power equipment image recognition is conductive to improving the inspection efficiency and quality in intelligent substations. However, few available labelled data and complex background largely increase the difficulty of identification. In this paper, an unsupervised learning algorithm based on contrastive loss and Transformer network is proposed to solve the small-sample learning problem. Contrastive loss is to build a dictionary where keys and queries are image patches of equipment. To enlarge the size of dictionary, the model implements it as a queue of samples and uses momentum updating strategy to ensure the encoder of key to be consistent. On top of that, Transformer network which is good at capturing context information in images is introduced and combined with contrastive loss. The key and query are those from encoder and decoder of Transformer network. Experimental results show that the proposed method realizes the unsupervised learning of power equipment image recognition task and outperforms other recent unsupervised strategies.
Research on vulnerable line identification based on PMU data and graph theory

Cascaded faults in complex power grids are prone to large-scale blackouts, and vulnerable transmission lines in cascading failures play an important role. Finding out these lines in power grid is very important to prevent accidents. The determination indicators of previous research methods are single. In this paper, a comprehensive analysis is carried out from two aspects of system operation structure and line power flow carrying capacity, and a vulnerable line identification method based on PMU data and graph theory is proposed. Firstly, a topology map of the power system is constructed in combination with the characteristics of PMU data visualization in actual power grid. Secondly, combined with power exchange efficiency and energy fluctuation probability, vulnerable lines of the grid are screened, and the effectiveness of the index is tested. Finally, the simulation experiment is carried out in the IEEE 30-bus system. Simulation results show that the proposed method can accurately identify weak transmission lines of the power grid.
Paste You Into Game: Towards Expression and Identity Consistency Face Swapping

Customizing game characters for individual players has been a long-standing attractive feature in the game industry. However, traditional solutions like manual editing within a game engine are always time-consuming and unsatisfying. Our work proposes a novel automatic face swapping method for arbitrary users and game characters, addressing three challenges including style gap between human and game faces, identity preservation, and expression consistency. A game face dataset is collected to handle the cross-style gap; an identity compound embedding is proposed to ease the bias existing in the commonly-used ID identifiers and it provides a more robust identity representation; a novel expression embedding loss is proposed to enforce the expression consistency between the swapped and target faces and it achieves better expression consistency than the previous methods, especially when the expression is very subtle. The visualized results, as well as the qualitative and quantitative comparisons, reveal the significance and effectiveness of our proposed solutions.
Memory-Augmented Episodic Value Network

In this paper, we propose memory-augmented episodic value network (M-EVN) to learn a differentiable planning-based policy with episodic memory in maze games. The episodic memory module associates the environmental state to its corresponding state value function, and outputs a weighted sum of state value functions with similar states to improve the agents’ navigation performance in partially observable mazes. In addition, we introduce a Net-in-Net architecture to make M-EVN differentiable by error backpropagation and learn an explicit planning computation. We train M-EVN in 2D maze games, and the experimental results show that the M-EVN agent outperforms the original value iteration network (VIN) in the partially observable maze games.
Towards verifiable Benchmarks for Reinforcement Learning

Reinforcement Learning (RL) is one of the most dynamic research areas in Game AI and AI as a whole, and a wide variety of games are used as its prominent test problems. However, it is subject to the replicability crisis that currently affects most algorithmic AI research. Benchmarking in Reinforcement Learning could be improved through verifiable results. There are numerous benchmark environments whose scores are used to compare different algorithms, such as Atari. Nevertheless, reviewers must trust that figures represent truthful values, as it is difficult to reproduce an exact training curve. We propose improving this situation by providing access to the original evaluation data to validate study results. To that end, we rely on the concept of replay traces. These allow re-simulation of action sequences in deterministic RL environments and, in turn, enable reviewers to verify, re-use, and manually inspect evaluation results without needing large compute clusters. It also permits validation of presented reward graphs, an inspection of individual episodes, and re-use of result data (baselines) for proper comparison in follow-up papers. We offer plug-and-play code that works with Gym so that our measures fit well in the existing RL and reproducibility eco-system. Our approach is freely available, easy to use, and adds minimal overhead, as replay traces allow a data compression ratio of up to $\approx 10^{4}$: 1 (94 GB to 8 MB for Atari Pong) compared to a regular MDP trace used in offline RL datasets. The paper presents proof-of-concept results for a variety of games.