Effects of Boron Application and Gamma Ray Irradiation On Yield of Sugar Beet (Beta Vulgaris L.) Grown in Sandy Soil

Effects of boron as an essential nutrient for sugar beet along with gamma irradiation was traced under field conditions. Data demonstrated that the fresh and dry root yields of sugar beet significantly increased with the increase either gamma dose or boron levels comparing to the non-irradiated and born-untreated plants. It seems that root dry weight tended to increase gradually with increasing gamma dose 0 to 100 Gy, then tended to decrease with 200 Gy dose but it still increased with gradual increases of boron levels. In this regard, the best value of root dry weight was achieved with 100 Gy dose interacted with 2.40 kg/ ha of boron addition. On the other hand, plants exposed to 50 Gy and treated with 2.40 kg/ ha reflected the highest shoot dry matter yield compared to other treatments. Relatively, this treatment increased shoot dry weight by about one fold, two folds and near to two folds for the same sequence. Behavior of NPK uptake by shoots has the same trend. Plants irradiated with 100 Gy together with 2.40 kg boric acid ha-1 yielded the highest total soluble solids (TSS%) and produced the best per cent of sucrose. Bangladesh J. Bot. 50(4): 1041-1049, 2021 (December)

Methods for Host-Based Intrusion Detection with Deep Learning

Host-based Intrusion Detection Systems (HIDS) automatically detect events that indicate compromise by adversarial applications. HIDS are generally formulated as analyses of sequences of system events such as bash commands or system calls. Anomaly-based approaches to HIDS leverage models of normal (aka baseline) system behavior to detect and report abnormal events, and have the advantage of being able to detect novel attacks. In this paper we develop a new method for anomaly-based HIDS using deep learning predictions of sequence-to-sequence behavior in system calls. Our proposed method, called the ALAD algorithm, aggregates predictions at the application level to detect anomalies. We investigate the use of several deep learning architectures, including WaveNet and several recurrent networks. We show that ALAD empowered with deep learning significantly outperforms previous approaches. We train and evaluate our models using an existing dataset, ADFA-LD, and a new dataset of our own construction, PLAID. As deep learning models are black box in nature we use an alternate approach, allotaxonographs, to characterize and understand differences in baseline vs.~attack sequences in HIDS datasets such as PLAID.

Manufacturing Information Recommendation Model Based on Pattern Mining

Existing enterprise information systems seldom consider different requirements from different users. To remedy this problem, the idea of manufacturing information active recommendation is put forward in this paper to deliver proper information to proper users correctly and timely. A new model called Sequence Behavior Access Pattern tree (SBAP-tree) is constructed based on the different requirements of user's identity, location, behavior habit and business needs for manufacturing information in Web environment. Using this SBAP-tree, historical situations similar to current situation could be sorted by their values, and the behavior could then be determined and output based on the association between the highly similar historical situations. Finally, an example is provided to demonstrate the effectiveness of SBAP-tree and the manufacturing information recommendation model based on SBAP-tree.

Research of Manufacturing Information Recommendation Model Based on Pattern Mining

Existing enterprises information systems seldom take different requirement tendency of different personnel into consideration. The idea of Manufacturing Information Active Recommendation was put forward to transfer proper information to proper personnel correctly and timely. The demand sequence behavior access pattern tree was constructed based on the different requirement of user's identity, location and business needs in Web environment manufacturing information. Historical situation similar to current situation could be sorted by its value, and the Behavior could be determined and output based on the association between the highly similar historical situations. Finally, an example was provided to demonstrate the effectiveness of the information modeling process frequent sequence behavior access pattern tree and the model.