Search Results (12)

One aspect of clausal embedding that has not received any specific attention in the literature is the question of whether and how derivational morphology may affect clausal selection properties of the respective bases. In this paper, I will focus on the role of German preverbs for clausal embedding. I will show that any parameter of clausal embedding can be affected by a preverb, though sometimes in a non-compositional way. Preverbs may affect presuppositions and entailments of their base verb, their selectional behavior with respect to clause types, their status as control or raising predicate and their potential for restructuring. Furthermore, preverbs may license or block neg-raising. The first part of the paper is dedicated to the demonstration of these effects with no specific preverb in mind. The second part discusses three specific preverb patterns with zu- ‘to’, ein- ‘in’ and er-, showing their specific clausal complementation properties. Preverbs influence clausal complementation by their impact on the argument structure/realization (in the case of control and restructuring) and on the lexical aspect of the base (in the case of certain interrogative complements and neg-raising). Full article

To address the issue of low accuracy in detecting malicious program behaviors in new power system edge-side applications, we present a detection model based on API call sequences that combines rule matching and deep learning techniques in this paper. We first use the PrefixSpan algorithm to mine frequent API call sequences in different threads of the same program within a malicious program dataset to create a rule base for malicious behavior sequences. The API call sequences to be examined are then matched using the malicious behavior sequence matching model, and those that do not match are fed into the TextCNN deep learning detection model for additional detection. The two models collaborate to accomplish program behavior detection. Experimental results demonstrate that the proposed detection model can effectively identify malicious samples and discern malicious program behaviors. Full article

Alarm systems are essential to the process safety and efficiency of complex industrial facilities. However, with the increasing size of plants and the growing complexity of industrial processes, alarm flooding is becoming a serious problem and posing challenges to alarm systems. Extracting alarm patterns from an alarm flood database can assist with an alarm root cause analysis, decision support, and the configuration of an alarm suppression model. However, due to the large size of the alarm database and the problem of sequence ambiguity in the alarm sequence, existing algorithms suffer from excessive computational overhead, incomplete alarm patterns, and redundant outputs. In order to solve these problems, we propose an alarm pattern extraction method based on the improved PrefixSpan algorithm. Firstly, a priority-based pre-matching strategy is proposed to cluster similar sequences in advance. Secondly, we improved PrefixSpan by considering timestamps to tolerate short-term order ambiguity in alarm flood sequences. Thirdly, an alarm pattern compression method is proposed for the further distillation of pattern information in order to output representative alarm patterns. Finally, we evaluated the effectiveness and applicability of the proposed method by using an alarm flood database from a real diesel hydrogenation unit. Full article

Product layout significantly impacts consumer demand for purchases in supermarkets. Product shelf renovation is a crucial process that can increase supermarket efficiency. The development of a sequential pattern mining algorithm for investigating the correlation patterns of product layouts, solving the numerous problems of shelf design, and the development of an algorithm that considers in-store purchase and shelf profit data with the goal of improving supermarket efficiency, and consequently profitability, were the goals of this research. The authors of this research developed two types of algorithms to enhance efficiency and reach the goals. The first was a PrefixSpan algorithm, which was used to optimize sequential pattern mining, known as the PrefixSpan mining approach. The second was a new multi-objective design that considered the objective functions of profit volumes and closeness rating using the mutation-based harmony search (MBHS) optimization algorithm, which was used to evaluate the performance of the first algorithm based on the PrefixSpan algorithm. The experimental results demonstrated that the PrefixSpan algorithm can determine correlation rules more efficiently and accurately ascertain correlation rules better than any other algorithms used in the study. Additionally, the authors found that MBHS with a new multi-objective design can effectively find the product layout in supermarket solutions. Finally, the proposed product layout algorithm was found to lead to higher profit volumes and closeness ratings than traditional shelf layouts, as well as to be more efficient than other algorithms. Full article

This work is motivated by growing evidence that the standard Cyclic Prefix (CP) length, adopted in the Long Term Evolution (LTE) physical layer (PHY) specifications, is oversized in propagation environments ranging from indoor to typical urban. Although this ostensibly seems to be addressed by 5G New Radio (NR) numerology, its scalable CP length reduction is proportionally tracked by the OFDM symbol length, which preserves the relative CP overhead of LTE. Furthermore, some simple means to optimize fixed or introduce adaptive CP length arose from either simulations or models taking into account only the bit-oriented PHY transmission performance. On the contrary, in the novel crosslayer analytical model proposed here, the closed-form expression for the optimal CP length is derived such as to minimize the effective average codeblock length, by also considering the error recovery retransmissions through the layers above PHY—the Medium Access Control (MAC) and the Radio Link Control (RLC), in particular. It turns out that, for given protective coding, the optimal CP length is determined by the appropriate rms delay spread of the channel power delay profile part remaining outside the CP span. The optimal CP length values are found to be significantly lower than the corresponding industry-standard ones, which unveils the potential for improving the net throughput. Full article

Real-word phenomena, such as ocean eddies and clouds, tend to split and merge while they are moving around within a space. Their trajectories usually bear one or more branches and are accordingly defined as complex trajectories in this study. The trajectories may show significant spatiotemporal variations in terms of their structures and some of them may be more prominent than the others. The identification of prominent structures in the complex trajectories of such real-world phenomena could better reveal their evolution processes and even shed new light on the driving factors behind them. Methods have been proposed for the extraction of periodic patterns from simple trajectories (i.e., those with linear structure and without any branches) with a focus on mining the related temporal, spatial or semantic information. Unfortunately, it is not appropriate to directly use such methods to examine complex trajectories. This study proposes a novel method to study the periodic patterns of complex trajectories by considering the inherent spatial, temporal and topological information. First, we use a sequence of symbols to represent the various structures of a complex trajectory over its lifespan. We then, on the basis of the PrefixSpan algorithm, propose a periodic pattern mining of structural evolution (PPSE) algorithm and use it to identify the largest and most frequent patterns (LFPs) from the symbol sequence. We also identify potential periodic behaviors. The PPSE method is then used to examine the complex trajectories of the mesoscale eddy in the South China Sea (SCS) from 1993 to 2016. The complex trajectories of ocean eddies in the southeast of Vietnam show are different from other regions in the SCS in terms of their structural evolution processes, as indicated by the LFPs with the longest lifespan, the widest active range, the highest complexity, and the most active behaviors. The LFP in the southeast of Vietnam has the longest lifespan, the widest active range, the highest complexity, and the most active behaviors. Across the SCS, we found seven migration channels. The LFPs of the eddies that migrate through these channels have a temporal cycle of 17–24 years. These channels are also the regions where eddies frequently emerge, as revealed by flow field data. Full article

Factories of the future are foreseen to evolve into smart factories with autonomous and adaptive manufacturing processes. However, the increasing complexity of the network of manufacturing processes is expected to complicate the rapid detection of process anomalies in real time. This paper proposes an architecture framework and method for the implementation of the Scalable On-line Anomaly Detection System (SOADS), which can detect process anomalies via real-time processing and analyze large amounts of process execution data in the context of autonomous and adaptive manufacturing processes. The design of this system architecture framework entailed the derivation of standard subsequence patterns using the PrefixSpan algorithm, a sequential pattern algorithm. The anomalies of the real-time event streams and derived subsequence patterns were scored using the Smith-Waterman algorithm, a sequence alignment algorithm. The excellence of the proposed system was verified by measuring the time for deriving subsequence patterns and by obtaining the anomaly scoring time from large event logs. The proposed system succeeded in large-scale data processing and analysis, one of the requirements for a smart factory, by using Apache Spark streaming and Apache Hbase, and is expected to become the basis of anomaly detection systems of smart factories. Full article

A non-homogeneous Poisson model was proposed to analyze the sequences of dry spells below prefixed thresholds as an upgrade of a stochastic procedure previously used to describe long periods of no rainfall. Its application concerned the daily precipitation series in a 60-year time span at four rain gauges (Calabria, southern Italy), aiming at testing the different behaviors of the dry spells below prefixed thresholds in two paired periods (1951–1980 and 1981–2010). A simulation analysis performed through a Monte Carlo approach assessed the statistical significance of the variation of the mean values of dry spells observed at an annual scale in the two 30-year periods. The results evidenced that the dry spells durations increased passing from the first 30-year period to the second one for all the thresholds analyzed. For instance, for the Cassano station, an increase of about 10% of the maximum dry spell duration was detected for a threshold of 5 mm. Moreover, the return periods evaluated for fixed long dry spells through the synthetic data of the period 1981–2010 were lower than the corresponding ones evaluated with the data generated for the previous 30-year period. Specifically, the difference between the two 30-year periods in terms of the return period of long dry spells occurrence increased with the growing thresholds. As an example, for the Cosenza rain gauge with a threshold of 1 mm, the return period for a dry spell length of 70 days decreased from 20 years (in 1951–1980) to about 10 years (in 1981–2010), while for a threshold of 5 mm, the return period for the dry spell lengths of 120 days decreases from 70 years to about 20 years. These results show a higher probability of the occurrence of long dry spells in the more recent period than in the past. Full article

Positioning devices allow users’ movement to be recorded. The GPS (Global Positioning System) trajectory data typically consists of spatiotemporal points, which make up the major part of the big data concerning urban life. Existing knowledge extraction methods about the trajectory share a general limitation—they only investigate data from a spatiotemporal aspect, but fail to take the semantic information of trajectories into consideration. Therefore, extracting the semantic information of trajectories with the context of big data is challenging pattern recognition task that has practical application prospects. In this paper, a system is proposed to extract the semantic trajectory patterns of positioning device users. Firstly, a spatiotemporal threshold and clustering based pre-processing model is proposed to process the raw data. Then, we design a probabilistic generative model to annotate the semantic information of each trajectory after the pre-processing procedure. Finally, we apply the PrefixSpan algorithm to mine the semantic trajectory patterns. We verify our system on a large dataset of users’ real trajectories over a period of 5 years in Beijing, China. The results of the experiment indicate that our system produces meaningful patterns. Full article

Wireless sensor networks (WSNs) are an important type of network for sensing the environment and collecting information. It can be deployed in almost every type of environment in the real world, providing a reliable and low-cost solution for management. Huge amounts of data are produced from WSNs all the time, and it is significant to process and analyze data effectively to support intelligent decision and management. However, the new characteristics of sensor data, such as rapid growth and frequent updates, bring new challenges to the mining algorithms, especially given the time constraints for intelligent decision-making. In this work, an efficient incremental mining algorithm for discovering sequential pattern (novel incremental algorithm, NIA) is proposed, in order to enhance the efficiency of the whole mining process. First, a reasoned proof is given to demonstrate how to update the frequent sequences incrementally, and the mining space is greatly narrowed based on the proof. Second, an improvement is made on PrefixSpan, which is a classic sequential pattern mining algorithm with a high-complexity recursive process. The improved algorithm, named PrefixSpan+, utilizes a mapping structure to extend the prefixes to sequential patterns, making the mining step more efficient. Third, a fast support number-counting algorithm is presented to choose frequent sequences from the potential frequent sequences. A reticular tree is constructed to store all the potential frequent sequences according to subordinate relations between them, and then the support degree can be efficiently calculated without scanning the original database repeatedly. NIA is compared with various kinds of mining algorithms via intensive experiments on the real monitoring datasets, benchmarking datasets and synthetic datasets from aspects including time cost, sensitivity of factors, and space cost. The results show that NIA performs better than the existed methods. Full article

The purpose of this paper is to predict failures based on textual sequence data. The current failure prediction is mainly based on structured data. However, there are many unstructured data in aircraft maintenance. The failure mentioned here refers to failure types, such as transmitter failure and signal failure, which are classified by the clustering algorithm based on the failure text. For the failure text, this paper uses the natural language processing technology. Firstly, segmentation and the removal of stop words for Chinese failure text data is performed. The study applies the word2vec moving distance model to obtain the failure occurrence sequence for failure texts collected in a fixed period of time. According to the distance, a clustering algorithm is used to obtain a typical number of fault types. Secondly, the failure occurrence sequence is mined using sequence mining algorithms, such as-PrefixSpan. Finally, the above failure sequence is used to train the Bayesian failure network model. The final experimental results show that the Bayesian failure network has higher accuracy for failure prediction. Full article

Aimed at reducing structural homogeneity and symmetrical competition in even-aged forest stands and enhancing stand structure diversity, the present study contributes to the design and implementation of adaptive silvicultural practices with two objectives: (1) preserving high wood production rates under changing environmental conditions and (2) ensuring key ecological services including carbon sequestration and forest health and vitality over extended stand life-spans. Based on a quantitative analysis of selected stand structure indicators, the experimental design was aimed at comparing customary practices of thinning from below over the full standing crop and innovative practices of crown thinning or selective thinning releasing a pre-fixed number of best phenotypes and removing direct crown competitors. Experimental trials were established at four beech forests along a latitudinal gradient in Italy: Cansiglio, Veneto; Vallombrosa, Tuscany; Chiarano, Abruzzo; and Marchesale, Calabria). Empirical results indicate a higher harvesting rate is associated with innovative practices compared with traditional thinning. A multivariate discriminant analysis outlined significant differences in post-treatment stand structure, highlighting the differential role of structural and functional variables across the study sites. These findings clarify the impact of former forest structure in shaping post-treatment stand attributes. Monitoring standing crop variables before and after thinning provides a basic understanding to verify intensity and direction of the applied manipulation, the progress toward the economic and ecological goals, as well as possible failures or need for adjustments within a comprehensive strategy of adaptive forest management. Full article