Historical Sequence Research Articles

A two-stage data-driven metaheuristic to predict last-mile delivery route sequences

On delivery operations, vehicle routing problems regularly focus on optimizing route-solution quality. However, the real-life execution of routes often differs from the computed routing plan. Therefore, the routing algorithms should use information from historic routes and drivers’ decisions to make the planned route as similar as possible to the actual executed route. We study the problem of predicting route sequences that drivers follow in last-mile parcel delivery in the context of the 2021 Amazon Last-Mile Routing Research Challenge. We propose extracting information from historic routes using a probability estimation method that learns transition probabilities between clusters or zones, coupled with a simple yet effective two-stage Greedy Randomized Adaptive Search Procedure (GRASP) to incorporate the information from historical route data into route planning. Our GRASP includes components in the objective function that consider decisions observed on the historical route sequences supplied by Amazon. We evaluate the predicted route sequences using the score similarity metric provided in the Amazon challenge. The results show that our method obtains a similarity score of 0.0328, which is comparable to the scores achieved by the three winning teams of the Amazon challenge. The proposed method successfully uses historical routes to predict route sequences on a last-mile delivery setting.

Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism

This study proposes a new approach for short-term power load forecasting using a combination of convolutional neural networks (CNN), long short-term memory (LSTM), and attention mechanisms to address the issue of information loss due to excessively long input time series data. The objective is to enhance the accuracy of short-term power load prediction, which is crucial for efficient energy management. The study analyzes the relationship between the target load and the collected parameters, identifying the most influential factors using Pearson correlation coefficient analysis. A one-dimensional CNN layer is utilized to extract high-dimensional features from the input data, followed by an LSTM layer that captures temporal correlations within the historical sequences. Finally, an attention mechanism is introduced to optimize the weight of the LSTM output, enhance the influence of key information, and optimize the overall prediction model. The performance of the proposed model is evaluated using two benchmark models based on mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) metrics. The results show that the CNN-LSTM-A model outperforms the traditional LSTM model regarding power load prediction accuracy for two thermal power units, with an improvement of 7.3% and 5.7%, respectively, indicating superior performance. Therefore, this study demonstrates the effectiveness of the proposed CNN-LSTM-A model for short-term power load forecasting, which has potential applications in the energy industry. In conclusion, the proposed approach can improve the accuracy of power load forecasting, leading to more efficient energy management and cost savings. Additionally, the study highlights the importance of incorporating attention mechanisms into traditional LSTM models for power load forecasting, as it helps to optimize the weight of the LSTM output and improve the accuracy of the predictions. The proposed CNN-LSTM-A model can be potentially useful for energy companies and policymakers in making informed decisions regarding energy production and consumption. Overall, this study provides a valuable contribution to power load forecasting, and the proposed approach could be extended to other areas of time-series forecasting in the future.

CORPORATE SOCIAL RESPONSIBILITY

As a rich expression of meanings and uses, the work reveals the meanings of Corporate Social Responsibility. The text presents, under the ballast of bibliographic review and the logical-inductive method, the historical sequence of meaning of the term and culminates in the exposition of conceptual model of management that combines economic and social aspects, with respect for human rights, and that has the environment as an imperative of responsibility. It exposes the eventual emergence in the step of a binding international document, in order to avoid a gravitating reputational label that can be coined as blue, green or other washings. The first result of the research is that companies today do not fulfill their social function merely by generating profits, and that, as a second result, it is necessary that corporate management derives its social license to create wealth and pursue economic policies and practices that promote the sustainable development of society. In conclusion, it should be noted that the concepts attributed to Corporate Social Responsibility are incomplete duo to variation in the meaning of the expression over time and that its parameters are still only ethical, since they lack other, legal, parameters that companies must comply with.

Deep Attentive Model for Knowledge Tracing

Knowledge Tracing (KT) is a crucial task in the field of online education, since it aims to predict students' performance on exercises based on their learning history. One typical solution for knowledge tracing is to combine the classic models in educational psychology, such as Item Response Theory (IRT) and Cognitive Diagnosis (CD), with Deep Neural Networks (DNN) technologies. In this solution, a student and related exercises are mapped into feature vectors based on the student's performance at the current time step, however, it does not consider the impact of historical behavior sequences, and the relationships between historical sequences and students. In this paper, we develop DAKTN, a novel model which assimilates the historical sequences to tackle this challenge for better knowledge tracing. To be specific, we apply a pooling layer to incorporate the student behavior sequence in the embedding layer. After that, we further design a local activation unit, which can adaptively calculate the representation vectors by taking the relevance of historical sequences into consideration with respect to candidate student and exercises. Through experimental results on three real-world datasets, DAKTN significantly outperforms state-of-the-art baseline models. We also present the reasonableness of DAKTN by ablation testing.

An attention-based Bayesian sequence to sequence model for short-term solar power generation prediction within decomposition-ensemble strategy

The utilization of renewable energy has attracted much attention with the deterioration of the environment. Solar energy is widely distributed and has huge reserves. But, solar energy is also volatile and intermittent. This brings difficulties to the large-scale grid connection of solar energy and the distribution of power resources. The key to solve this problem is to provide accurate and stable short-term prediction for solar power generation. For this purpose, we present an attention-based Bayesian sequence to sequence (Seq2Seq) model within decomposition-ensemble strategy. Firstly, an influential factors analysis is performed on the data about meteorological conditions, operation status of the photovoltaic panels and historical solar power generation sequences to obtain the optimal combination of the influential factors. Secondly, this paper proposes a novel decomposition-ensemble framework based on complete ensemble empirical mode decomposition with adaptive noise and independent component analysis to mine the intrinsic modes of the solar power generation time series. Thirdly, this paper presents an attention-based Bayesian Seq2Seq method for modeling the relationships between solar power generation and influential factors. Using a real-world dataset from State Administration of Electricity Investment Science and Technology of China, a case study, comparative analysis and robustness checks are conducted, through which the contributions of the methods in the proposed model and the superior performance of the model are demonstrated.

Rertraction Notice: Conceptual Reflection in Its Main Historical and Philosophical Sequence. 2012; (3); 25-33.

The article Conceptual Reflection in Its Main Historical and Philosophical Sequence published in the Bulletin of Nizhnevartovsk State University of Humanities (the Bulletin of Nizhnevartovsk State University since 2013) (2012, Vol. 3) by V.I. Krasikov is a duplicate of the article by V.I. Krasikov Philosophy: Its Mental and Social Nature, Main Historical Sequences published in the Bulletin of Kemerovo State University (2012, Vol.1). That was the reason for retraction.

Multi-Step-Ahead Wind Speed Forecast Method Based on Outlier Correction, Optimized Decomposition, and DLinear Model

Precise and dependable wind speed forecasting (WSF) enables operators of wind turbines to make informed decisions and maximize the use of available wind energy. This study proposes a hybrid WSF model based on outlier correction, heuristic algorithms, signal decomposition methods, and DLinear. Specifically, the hybrid model (HI-IVMD-DLinear) comprises the Hampel identifier (HI), the improved variational mode decomposition (IVMD) optimized by grey wolf optimization (GWO), and DLinear. Firstly, outliers in the wind speed sequence are detected and replaced with the HI to mitigate their impact on prediction accuracy. Next, the HI-processed sequence is decomposed into multiple sub-sequences with the IVMD to mitigate the non-stationarity and fluctuations. Finally, each sub-sequence is predicted by the novel DLinear algorithm individually. The predictions are reconstructed to obtain the final wind speed forecast. The HI-IVMD-DLinear is utilized to predict the real historical wind speed sequences from three regions so as to assess its performance. The experimental results reveal the following findings: (a) HI could enhance prediction accuracy and mitigate the adverse effects of outliers; (b) IVMD demonstrates superior decomposition performance; (c) DLinear has great prediction performance and is suited to WSF; and (d) overall, the HI-IVMD-DLinear exhibits superior precision and stability in one-to-four-step-ahead forecasting, highlighting its vast potential for application.

DeepTrip: A Deep Learning Model for the Individual Next Trip Prediction With Arbitrary Prediction Times

The increasing availability of travel trajectory data allows for a better understanding of travel behavior. In the individual mobility analysis, the problem of next trip prediction assumes a central role and is beneficial for applications such as personalized services and mobility management. This paper addresses the next trip prediction problem with arbitrary prediction times (the time when the prediction is made). This problem has not been studied adequately in the literature and it is important for applications driven by system events, such as proactive travel recommendations under disruptions or crowding in transport systems. It predicts an individual’s next trips given their historical trip sequences and the prediction time. We formulate the next trip prediction problem as on-board and off-board predictions depending on an individual’s travel status (i.e. on-board/off-board). Using historical/real-time travel trajectories, a DeepTrip model is proposed based on a trip sequence-to-sequence deep learning structure coupled with an attention mechanism. A novel overlapped embedding method is proposed to represent continuous travel attributes capturing simultaneously the categorical and numerical feature information. We also develop a random-sampling training algorithm to learn the impact of the prediction time. The model is validated using trip data in urban rails. The results show that DeepTrip outperforms statistical-based models by more than 10% in terms of accuracy and other deep learning models by 2%-3%. The impact analysis shows that different representations are appropriate for the two prediction cases (on-board/off-board), and the prediction performance does not monotonically improve as the prediction time approaches the next trip.

كلمة العدد (الإنجليزي)

كلمة العدد (الإنجليزي)

FCP2Vec: Deep Learning-Based Approach to Software Change Prediction by Learning Co-Changing Patterns from Changelogs

As software systems evolve, they become more complex and larger, creating challenges in predicting change propagation while maintaining system stability and functionality. Existing studies have explored extracting co-change patterns from changelog data using data-driven methods such as dependency networks; however, these approaches suffer from scalability issues and limited focus on high-level abstraction (package level). This article addresses these research gaps by proposing a file-level change propagation to vector (FCP2Vec) approach. FCP2Vec is a recommendation system designed to aid developers by suggesting files that may undergo change propagation subsequently, based on the file being presently worked on. We carried out a case study utilizing three publicly available datasets: Vuze, Spring Framework, and Elasticsearch. These datasets, which consist of open-source Java-based software development changelogs, were extracted from version control systems. Our technique learns the historical development sequence of transactional software changelog data using a skip-gram method with negative sampling and unsupervised nearest neighbors. We validate our approach by analyzing historical data from the software development changelog for more than ten years. Using multiple metrics, such as the normalized discounted cumulative gain at K (NDCG@K) and the hit ratio at K (HR@K), we achieved an average HR@K of 0.34 at the file level and an average HR@K of 0.49 at the package level across the three datasets. These results confirm the effectiveness of the FCP2Vec method in predicting the next change propagation from historical changelog data, addressing the identified research gap, and show a 21% better accuracy than in the previous study at the package level.

Image feature learning combined with attention‐based spectral representation for spatio‐temporal photovoltaic power prediction

AbstractClean energy is a major trend. The importance of photovoltaic power generation is also growing. Photovoltaic power generation is mainly affected by the weather. It is full of uncertainties. Previous work has relied chiefly on historical photovoltaics data for time series forecasts. However, unforeseen weather conditions can sometimes skew. Consequently, a spatial‐temporal‐meteorological‐long short‐term memory prediction model (STM‐LSTM) is proposed to compensate for the shortage of photovoltaic prediction models for uncertainties. This model can simultaneously process satellite image data, historical meteorological data, and historical power generation data. In this way, historical patterns and meteorological change information are extracted to improve the accuracy of photovoltaic prediction. STM‐LSTM processes raw satellite data to obtain cloud image data. It can extract cloud motion information using the dense optical flow method. First, the cloud images are processed to extract cloud position information. By adaptive attentive learning of images in different bands, a better representation for subsequent tasks can be obtained. Second, it is important to process historical meteorological data to learn meteorological change patterns. Last but not least, the historical photovoltaic power generation sequences are combined to obtain the final photovoltaic prediction results. After a series of experimental validation, the performance of the proposed STM‐LSTM model has a good improvement compared with the baseline model.

Multi-scale modeling temporal hierarchical attention for sequential recommendation

Multi-scale modeling of items interacting in a sequence of users' historical behaviors in a sequential recommendation task is crucial. In real scenarios, the user's choice of items depends not only on static preferences (long-term interests) but also on recent dynamic preferences (short-term interests). In recent years, micro-video sharing platforms have been very favorable, and correspondingly, more efficient recommendation method will be needed to support users in finding their interested micro-videos (items). Compared to traditional online videos (such as YouTube), micro-videos are created by grassroots users, shot by smartphones, are short (typically tens of seconds), and have fewer tags or descriptive text, which makes recommending it a challenging task. In this work, we explore how to model users' historical behavior sequences at multiple scales to predict their click-through rates on micro-videos and then determine whether to recommend them to users. We present a novel Multi-scale Modeling Temporal Hierarchical Attention (MMTHA) method for modeling users' behavior sequences inspired by recent deep network-based approaches. Specifically, firstly, we capture users' short-term dynamic interests using temporal windows; secondly, we utilize a category-level attention mechanism to describe the coarse-grained interests of users and an item-level attention mechanism to capture the fine-grained interests of users; thirdly, we employ a forward multi-headed self-attention mechanism to identify and integrate long-term correlations between previously segmented temporal windows. We conducted extensive experiments on two publicly available datasets to verify their effectiveness. The experimental results show that our proposed MMTHA model achieves state-of-the-art performance in all tests.

A Tourist Attractions Recommendation Model Based on Sequence Interest Extraction and Location Information Fusion

Travel, as one way to relax oneself, has become the first choice for people to enjoy their body and mind in modern society. However, while facing lots of information, how to help users make better decisions on their next travel goals through their historical interest spots is a direction that needs further research in big data recommendation systems. In this thesis, we proposed the deep convolution and multi-head self-attention position network model. First, it extracts the user’s historical interest point feature information by convolutional neural network method, and then performs horizontal and vertical filtering. Next, it interacts the obtained information with the candidate attraction information, and extracts the location information of the historical interest sequence by the multi-head self-attention mechanism. Finally, the model does the attention mechanism of the candidate attraction by fusing the feature information of the location information. The final model achieves a deep fusion of user sequence interest and location feature information. We conducted detailed comparison experiments with the very popular models in the industry on different public datasets, and the results showed that our deep convolution and multi-head self-attention position network model has good performance.

Historical Mitogenomic Diversity and Population Structuring of Southern Hemisphere Fin Whales.

Fin whales Balaenoptera physalus were hunted unsustainably across the globe in the 19th and 20th centuries, leading to vast reductions in population size. Whaling catch records indicate the importance of the Southern Ocean for this species; approximately 730,000 fin whales were harvested during the 20th century in the Southern Hemisphere (SH) alone, 94% of which were at high latitudes. Genetic samples from contemporary whales can provide a window to past population size changes, but the challenges of sampling in remote Antarctic waters limit the availability of data. Here, we take advantage of historical samples in the form of bones and baleen available from ex-whaling stations and museums to assess the pre-whaling diversity of this once abundant species. We sequenced 27 historical mitogenomes and 50 historical mitochondrial control region sequences of fin whales to gain insight into the population structure and genetic diversity of Southern Hemisphere fin whales (SHFWs) before and after the whaling. Our data, both independently and when combined with mitogenomes from the literature, suggest SHFWs are highly diverse and may represent a single panmictic population that is genetically differentiated from Northern Hemisphere populations. These are the first historic mitogenomes available for SHFWs, providing a unique time series of genetic data for this species.

Enhancing Multi-View Smoothness for Sequential Recommendation Models

Sequential recommendation models aim to predict the interested items to a user based on his historical behaviors. To train sequential recommenders, implicit feedback data is widely adopted since it is easier to obtain than explicit feedback data. In the setting of implicit feedback, a user’s historical behaviors can be characterized as a chronologically ordered sequence of interacted items. From a perspective of machine learning, the historical interaction sequence and the recommended items can be considered as context and label , respectively, which are usually in one-hot representations in the recommendation models. However, due to the discrete nature, one-hot representations are hard to sufficiently reflect the underlying user preference, and might also contain noise from implicit feedback that will mislead the model training. To solve these issues, we propose a general optimization framework, Multi-View Smoothness (MVS), to enhance the smoothness of sequential recommendation models in both data representations and model learning. Specifically, with the help of a complementary model, we smooth and enrich the one-hot representations of contexts and labels to better depict the underlying user preference (i.e., context smoothness and label smoothness), and devise a model regularization strategy to enforce the neighborhood smoothness of the model itself (i.e., model smoothness). Based on these strategies, we design three regularizers to constrain and improve the training of sequential recommendation models. Extensive experiments on five datasets show that our approach is able to improve the performance of various base models consistently and outperform other regularization training methods.

Recognize News Transition from Collective Behavior for News Recommendation

In the news recommendation, users are overwhelmed by thousands of news daily, which makes the users’ behavior data have high sparsity. Therefore, only considering a single user’s personalized preferences cannot support the news recommendation. How to improve the relatedness of news and users and reduce data sparsity has become a hot issue. Recent studies have attempted to use graph models to enrich the relationship between users and news, but they are still limited to modeling the historical behaviors of a single user. To fill the gap, we integrate user-news relationships and the overall user historical clicked news sequences to construct a global heterogeneous transition graph. And a refinement approach is proposed to recognize the news transition patterns in the graph. Based on the global heterogeneous transition graph, we propose a heterogeneous transition graph attention network to capture the common behavior patterns of most users to enhance the representation of user interest. Fusing the users’ personalized and common interest, we propose the GAINRec model to recommend news effectively. Extensive experiments are conducted on two public news recommendation datasets, and the results show the superiority of the proposed GAINRec model compared with the state-of-the-art news recommendation models. The implementation of our model is available at https://github.com/newsrec/GAINRec .

Regrettable experiences and the affirmation of life

My theme in this essay is the relation of misfortune – and other occasions for regret – to the affirmation of life. R. Jay Wallace believes there is an antagonistic relation that produces a schism between our affirmative attitudes and our reasons and considered judgments. On his view, our attachments to the persons and projects that give meaning to our lives lead us to affirm states of affairs it would be more appropriate to regret. I argue that the attitude of affirmation can acknowledge and do justice to reasons for regret. Wallace fails to appreciate the implications of the Nietzschean positions he cites as precedents; he assumes the past has a meaning that is fixed and insulated from the future. Drawing on Nietzsche, I argue that the affirmation of life is a coherent project of bestowing meaning on a historical sequence going forward into the future.

Two Types of Ritual Space at the Poverty Point Site 16WC5

AbstractTwo types of features at the Late Archaic period Poverty Point site in Louisiana—large timber post circles and repetitively used activity surfaces later covered by mounding—are examined as spaces that were the products of historical event sequences involving their construction, their entwined but differing ritual uses, and their final deconstruction or removal. This approach is a particularizing, historicizing way of approaching the features of this monumental hunter-gatherer site, an alternative to a more generalizing view in which its totality as a site or “place” is seen as the interrelated product of many historical acts over time or as a mosaic of historically created and used “spaces.” Analysis of these feature types suggests that spaces of ritual activity in the larger site were used differently, involving different types of events and serving different functions in a developing, perhaps shifting, larger ritual landscape.

Incorporating Multivariate Auxiliary Information for Traffic Prediction on Highways.

Traffic flow prediction is one of the most important tasks of the Intelligent Transportation Systems (ITSs) for traffic management, and it is also a challenging task affected by many complex factors, such as weather and time. Many cities adopt efficient traffic prediction methods to control traffic congestion. However, most of the existing methods of traffic prediction focus on urban road scenarios, neglecting the complexity of multivariate auxiliary information in highways. Moreover, these methods have difficulty explaining the prediction results based only on the historical traffic flow sequence. To tackle these problems, we propose a novel traffic prediction model, namely Multi-variate and Multi-horizon prediction based on Long Short-Term Memory (MMLSTM). MMLSTM can effectively incorporate auxiliary information, such as weather and time, based on a strategy of multi-horizon time spans to improve the prediction performance. Specifically, we first exploit a multi-horizon bidirectional LSTM model for fusing the multivariate auxiliary information in different time spans. Then, we combine an attention mechanism and multi-layer perceptron to conduct the traffic prediction. Furthermore, we can use the information of multivariate (weather and time) to provide interpretability to manage the model. Comprehensive experiments are conducted on Hangst and Metr-la datasets, and MMLSTM achieves better performance than baselines on traffic prediction tasks.

Kaiser, Christ, and Canaan: The Religion of Israel in Protestant Germany, 1871–1918. By Paul Michael Kurtz

Whoever seeks to place Julius Wellhausen (1844–1918) in the German Kaiserzeit may want to look at the magisterial volume on the Christian religion in a multi-volume work Die Kultur der Gegenwart. Ihre Entwicklung und Ziele, edited by Paul Hinneberg. The volume in question, published in 1906, comes under the title Die christliche Religion. Mit Einschluss der israelitisch-jüdischen Religion, and comprises two parts: ‘I. Geschichte der christlichen Religion’, and ‘II. Systematische christliche Theologie’. Part I starts with an introduction, ‘Die israelitisch-jüdische Religion’, by Wellhausen (pp. 1–40); Part II, with an introduction, ‘Wesen der Religion und der Religionswissenschaft’, by Ernst Troeltsch (pp. 461–91). More than a quarter of a century after his study of the historical sequence of the several collections of ritual laws in the Torah (published in 1878), Wellhausen is eager to emphasize that the religion of Israel in antiquity belongs in the category of religions which have, in fact, undergone a development in historical time. His Kaiserzeit audience would have been interested in an outline of such a religious development in ancient Israel as well as in an explanation of how Christianity in turn developed from its early foundation in ancient Judaism. One may assume that such an orientation has characterized religious, and more particularly biblical, studies ever since the end of a purely allegorical and typological reading of the Hebrew Bible, but it is not amiss to claim that German scholarship in the Kaiserzeit had a strong interest in constructing historical knowledge about the past, including the remote past. Whether there was anything ‘German’ about this may be discussed, especially since in the case of Wellhausen the ‘Dutch’ scholar Abraham Kuenen (1828–91) or the ‘Scottish’ scholar William Robertson Smith (1846–94), for example, were among colleagues with whom Wellhausen exchanged a considerable number of letters. The correspondence has become fully available in an edition of 2013, and Kurtz refers extensively, though not consistently, to this material.