PeerJ Computer Science最新文献

{"title":"Efficient anomaly recognition using surveillance videos.","authors":"Gulshan Saleem,&nbsp;Usama Ijaz Bajwa,&nbsp;Rana Hammad Raza,&nbsp;Fayez Hussain Alqahtani,&nbsp;Amr Tolba,&nbsp;Feng Xia","doi":"10.7717/peerj-cs.1117","DOIUrl":"https://doi.org/10.7717/peerj-cs.1117","url":null,"abstract":"<p><p>Smart surveillance is a difficult task that is gaining popularity due to its direct link to human safety. Today, many indoor and outdoor surveillance systems are in use at public places and smart cities. Because these systems are expensive to deploy, these are out of reach for the vast majority of the public and private sectors. Due to the lack of a precise definition of an anomaly, automated surveillance is a challenging task, especially when large amounts of data, such as 24/7 CCTV footage, must be processed. When implementing such systems in real-time environments, the high computational resource requirements for automated surveillance becomes a major bottleneck. Another challenge is to recognize anomalies accurately as achieving high accuracy while reducing computational cost is more challenging. To address these challenge, this research is based on the developing a system that is both efficient and cost effective. Although 3D convolutional neural networks have proven to be accurate, they are prohibitively expensive for practical use, particularly in real-time surveillance. In this article, we present two contributions: a resource-efficient framework for anomaly recognition problems and two-class and multi-class anomaly recognition on spatially augmented surveillance videos. This research aims to address the problem of computation overhead while maintaining recognition accuracy. The proposed Temporal based Anomaly Recognizer (TAR) framework combines a partial shift strategy with a 2D convolutional architecture-based model, namely MobileNetV2. Extensive experiments were carried out to evaluate the model's performance on the UCF Crime dataset, with MobileNetV2 as the baseline architecture; it achieved an accuracy of 88% which is 2.47% increased performance than available state-of-the-art. The proposed framework achieves 52.7% accuracy for multiclass anomaly recognition on the UCF Crime2Local dataset. The proposed model has been tested in real-time camera stream settings and can handle six streams simultaneously without the need for additional resources.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1117"},"PeriodicalIF":3.8,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575851/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40656160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep reinforcement learning based offloading decision algorithm for vehicular edge computing.","authors":"Xi Hu,&nbsp;Yang Huang","doi":"10.7717/peerj-cs.1126","DOIUrl":"https://doi.org/10.7717/peerj-cs.1126","url":null,"abstract":"<p><p>Task offloading decision is one of the core technologies of vehicular edge computing. Efficient offloading decision can not only meet the requirements of complex vehicle tasks in terms of time, energy consumption and computing performance, but also reduce the competition and consumption of network resources. Traditional distributed task offloading decision is made by vehicles based on local states and can't maximize the resource utilization of Mobile Edge Computing (MEC) server. Moreover, the mobility of vehicles is rarely taken into consideration for simplification. This article proposes a deep reinforcement learning based task offloading decision algorithm, namely Deep Reinforcement learning based offloading decision (DROD) for Vehicular Edge Computing (VEC). In this work, the mobility of vehicles and the signal blocking commonly in VEC circumstance are considered in our optimal problem of minimizing the system overhead. For resolving the optimal problem, the DROD employs Markov decision process to model the interactions between vehicles and MEC server, and an improved deep deterministic policy gradient algorithm called NLDDPG to train the model iteratively to obtain the optimal decision. The NLDDPG takes the normalized state space as input and introduces LSTM structure into the actor-critic network for improving the efficiency of learning. Finally, two series of experiments are conducted to explore DROD. Firstly, the influences of core hyper-parameters on the performances of DROD are discussed, and the optimal values are determined. Secondly, the DROD is compared with some other baseline algorithms, and the results show that DROD is 25% better than DQN, 10% better than NLDQN and 130% better than DDDPG.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1126"},"PeriodicalIF":3.8,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40644332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On component-wise dissimilarity measures and metric properties in pattern recognition.","authors":"Enrico De Santis,&nbsp;Alessio Martino,&nbsp;Antonello Rizzi","doi":"10.7717/peerj-cs.1106","DOIUrl":"https://doi.org/10.7717/peerj-cs.1106","url":null,"abstract":"<p><p>In many real-world applications concerning pattern recognition techniques, it is of utmost importance the automatic learning of the most appropriate dissimilarity measure to be used in object comparison. Real-world objects are often complex entities and need a specific representation grounded on a composition of different heterogeneous features, leading to a non-metric starting space where Machine Learning algorithms operate. However, in the so-called unconventional spaces a family of dissimilarity measures can be still exploited, that is, the set of component-wise dissimilarity measures, in which each component is treated with a specific sub-dissimilarity that depends on the nature of the data at hand. These dissimilarities are likely to be non-Euclidean, hence the underlying dissimilarity matrix is not isometrically embeddable in a standard Euclidean space because it may not be structurally rich enough. On the other hand, in many metric learning problems, a component-wise dissimilarity measure can be defined as a weighted linear convex combination and weights can be suitably learned. This article, after introducing some hints on the relation between distances and the metric learning paradigm, provides a discussion along with some experiments on how weights, intended as mathematical operators, interact with the Euclidean behavior of dissimilarity matrices.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1106"},"PeriodicalIF":3.8,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40573312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A block cipher algorithm identification scheme based on hybrid k-nearest neighbor and random forest algorithm.","authors":"Ke Yuan,&nbsp;Daoming Yu,&nbsp;Jingkai Feng,&nbsp;Longwei Yang,&nbsp;Chunfu Jia,&nbsp;Yiwang Huang","doi":"10.7717/peerj-cs.1110","DOIUrl":"https://doi.org/10.7717/peerj-cs.1110","url":null,"abstract":"<p><p>Cryptographic algorithm identification, which refers to analyzing and identifying the encryption algorithm used in cryptographic system, is of great significance to cryptanalysis. In order to improve the accuracy of identification work, this article proposes a new ensemble learning-based model named hybrid k-nearest neighbor and random forest (HKNNRF), and constructs a block cipher algorithm identification scheme. In the ciphertext-only scenario, we use NIST randomness test methods to extract ciphertext features, and carry out binary-classification and five-classification experiments on the block cipher algorithms using proposed scheme. Experiments show that when the ciphertext size and other experimental conditions are the same, compared with the baselines, the HKNNRF model has higher classification accuracy. Specifically, the average binary-classification identification accuracy of HKNNRF is 69.5%, which is 13%, 12.5%, and 10% higher than the single-layer support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) respectively. The five-classification identification accuracy can reach 34%, which is higher than the 21% accuracy of KNN, the 22% accuracy of RF and the 23% accuracy of SVM respectively under the same experimental conditions.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1110"},"PeriodicalIF":3.8,"publicationDate":"2022-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40644335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resilience of political leaders and healthcare organizations during COVID-19.","authors":"Manmeet Kaur Baxi,&nbsp;Joshua Philip,&nbsp;Vijay Mago","doi":"10.7717/peerj-cs.1121","DOIUrl":"https://doi.org/10.7717/peerj-cs.1121","url":null,"abstract":"<p><p>This study assesses the online societal association of leaders and healthcare organizations from the top-10 COVID-19 resilient nations through public engagement, sentiment strength, and inclusivity and diversity strength. After analyzing 173,071 Tweets authored by the leaders and health organizations, our findings indicate that United Arab Emirate's Prime Minister had the highest online societal association (normalized online societal association: 1.000) followed by the leaders of Canada and Turkey (normalized online societal association: 0.068 and 0.033, respectively); and among the healthcare organizations, the Public Health Agency of Canada was the most impactful (normalized online societal association: 1.000) followed by the healthcare agencies of Turkey and Spain (normalized online societal association: 0.632 and 0.094 respectively). In comparison to healthcare organizations, the leaders displayed a strong awareness of individual factors and generalized their Tweets to a broader audience. The findings also suggest that users prefer accessing social media platforms for information during health emergencies and that leaders and healthcare institutions should realize the potential to use them effectively.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1121"},"PeriodicalIF":3.8,"publicationDate":"2022-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40656162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AlphaDDA: strategies for adjusting the playing strength of a fully trained AlphaZero system to a suitable human training partner.","authors":"Kazuhisa Fujita","doi":"10.7717/peerj-cs.1123","DOIUrl":"https://doi.org/10.7717/peerj-cs.1123","url":null,"abstract":"<p><p>Artificial intelligence (AI) has achieved superhuman performance in board games such as Go, chess, and Othello (Reversi). In other words, the AI system surpasses the level of a strong human expert player in such games. In this context, it is difficult for a human player to enjoy playing the games with the AI. To keep human players entertained and immersed in a game, the AI is required to dynamically balance its skill with that of the human player. To address this issue, we propose AlphaDDA, an AlphaZero-based AI with dynamic difficulty adjustment (DDA). AlphaDDA consists of a deep neural network (DNN) and a Monte Carlo tree search, as in AlphaZero. AlphaDDA learns and plays a game the same way as AlphaZero, but can change its skills. AlphaDDA estimates the value of the game state from only the board state using the DNN. AlphaDDA changes a parameter dominantly controlling its skills according to the estimated value. Consequently, AlphaDDA adjusts its skills according to a game state. AlphaDDA can adjust its skill using only the state of a game without any prior knowledge regarding an opponent. In this study, AlphaDDA plays Connect4, Othello, and 6x6 Othello with other AI agents. Other AI agents are AlphaZero, Monte Carlo tree search, the minimax algorithm, and a random player. This study shows that AlphaDDA can balance its skill with that of the other AI agents, except for a random player. AlphaDDA can weaken itself according to the estimated value. However, AlphaDDA beats the random player because AlphaDDA is stronger than a random player even if AlphaDDA weakens itself to the limit. The DDA ability of AlphaDDA is based on an accurate estimation of the value from the state of a game. We believe that the AlphaDDA approach for DDA can be used for any game AI system if the DNN can accurately estimate the value of the game state and we know a parameter controlling the skills of the AI system.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1123"},"PeriodicalIF":3.8,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40646375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of soil type in Pakistan using remote sensing and machine learning.","authors":"Yasin Ul Haq,&nbsp;Muhammad Shahbaz,&nbsp;Hm Shahzad Asif,&nbsp;Ali Al-Laith,&nbsp;Wesam Alsabban,&nbsp;Muhammad Haris Aziz","doi":"10.7717/peerj-cs.1109","DOIUrl":"https://doi.org/10.7717/peerj-cs.1109","url":null,"abstract":"<p><p>Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1109"},"PeriodicalIF":3.8,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575843/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40644331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A trajectory data compression algorithm based on spatio-temporal characteristics.","authors":"Yanling Zhong,&nbsp;Jinling Kong,&nbsp;Juqing Zhang,&nbsp;Yizhu Jiang,&nbsp;Xiao Fan,&nbsp;Zhuoyue Wang","doi":"10.7717/peerj-cs.1112","DOIUrl":"https://doi.org/10.7717/peerj-cs.1112","url":null,"abstract":"<p><strong>Background: </strong>With the growth of trajectory data, the large amount of data causes a lot of problems with storage, analysis, mining, <i>etc</i>. Most of the traditional trajectory data compression methods are focused on preserving spatial characteristic information and pay little attention to other temporal information on trajectory data, such as speed change points or stop points.</p><p><strong>Methods: </strong>A data compression algorithm based on the spatio-temporal characteristics (CASC) of the trajectory data is proposed to solve this problem. This algorithm compresses trajectory data by taking the azimuth difference, velocity difference and time interval as parameters in order to preserve spatial-temporal characteristics. Microsoft's Geolife1.3 data set was used for a compression test to verify the validity of the algorithm. The compression results were compared with the traditional Douglas-Peucker (DP), Top-Down Time Ratio (TD-TR) and Opening Window (OPW) algorithms. Compression rate, the direction information of trajectory points, vertical synchronization distance, and algorithm type (online/offline) were used to evaluate the above algorithms.</p><p><strong>Results: </strong>The experimental results show that with the same compression rate, the ability of the CASC to retain the forward direction trajectory is optimal, followed by TD-TR, DP, and then OPW. The velocity characteristics of the trajectories are also stably retained when the speed threshold value is not more than 100%. Unlike the DP and TD-TR algorithms, CASC is an online algorithm. Compared with OPW, which is also an online algorithm, CASC has better compression quality. The error distributions of the four algorithms have been compared, and CASC is the most stable algorithm. Taken together, CASC outperforms DP, TD-TR and OPW in trajectory compression.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1112"},"PeriodicalIF":3.8,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40656164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on melanoma image segmentation by incorporating medical prior knowledge.","authors":"Hong Zhao,&nbsp;Aolong Wang,&nbsp;Chenpeng Zhang","doi":"10.7717/peerj-cs.1122","DOIUrl":"https://doi.org/10.7717/peerj-cs.1122","url":null,"abstract":"<p><strong>Background: </strong>Melanoma image segmentation has important clinical value in the diagnosis and treatment of skin diseases. However, due to the difficulty of obtaining data sets, and the sample imbalance, the quality of melanoma image data sets is low, which reduces the accuracy and the effectiveness of computer aided diagnosis of melanoma image.</p><p><strong>Objective: </strong>In this work, a method of melanoma image segmentation by incorporating medical prior knowledge is proposed to improve the fidelity of melanoma image segmentation.</p><p><strong>Methods: </strong>Anatomical analysis of the melanoma image reveal the star shape of the melanoma image, which can be encoded into the loss function of the UNet model as a prior knowledge.</p><p><strong>Results: </strong>Our experimental results on the ISIC-2017 data set demonstrate that the model by incorporating medical prior knowledge obtain a mIoU (Mean Intersection over Union) of 87.41%, a Dice Similarity Coefficient of 93.49%.</p><p><strong>Conclusion: </strong>Therefore, the model by incorporating medical prior knowledge achieve the first rank in the segmentation task comparing to other models and has high clinical value.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1122"},"PeriodicalIF":3.8,"publicationDate":"2022-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40558137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A DBULSTM-Adaboost Model for Sea Surface Temperature Prediction.","authors":"Jiachen Yang,&nbsp;Jiaming Huo,&nbsp;Jingyi He,&nbsp;Taiqiu Xiao,&nbsp;Desheng Chen,&nbsp;Yang Li","doi":"10.7717/peerj-cs.1095","DOIUrl":"https://doi.org/10.7717/peerj-cs.1095","url":null,"abstract":"<p><p>Sea surface temperature (SST) is an important parameter to measure the energy and heat balance of sea surface. The change of sea surface temperature has an important impact on the marine ecosystem, marine climate and marine environment. Therefore, sea surface temperature prediction has become an significant research direction in the field of ocean. This article proposes a DBULSTM-Adaboost model based on ensemble learning. The model is composed of Deep Bidirectional and Unidirectional Long Short Term Memory (DBULSTM) and Adaboost strong learner. DBULSTM can capture the forward and backward dependence of time series, and the DBULSTM model is integrated with Adaboost strong learner to reduce the variance and bias of prediction and realize the short and medium term prediction of SST at a single point scale. Experimental results show that the model can improve the accuracy and stability of SST prediction. Experiments on the East China Sea and South China Sea with different prediction lengths show that the model is almost superior to other classical models in different sea areas and at different prediction levels. Compared with full-connected LSTM (FC-LSTM) model, the root-mean-square error is reduced by about 0.1.</p>","PeriodicalId":503620,"journal":{"name":"PeerJ Computer Science","volume":" ","pages":"e1095"},"PeriodicalIF":3.8,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40558134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}