An Improved End-to-End Memory Network for QA Tasks

At present, End-to-End trainable Memory Networks (MemN2N) has proven to be promising in many deep learning fields, especially on simple natural language-based reasoning question and answer (QA) tasks. However, when solving some subtasks such as basic induction, path finding or time reasoning tasks, it remains challenging because of limited ability to learn useful information between memory and query. In this paper, we propose a novel gated linear units (GLU) and local-attention based end-to-end memory networks (MemN2N-GL) motivated by the success of attention mechanism theory in the field of neural machine translation, it shows an improved possibility to develop the ability of capturing complex memory-query relations and works better on some subtasks. It is an improved end-to-end memory network for QA tasks. We demonstrate the effectiveness of these approaches on the 20 bAbI dataset which includes 20 challenging tasks, without the use of any domain knowledge. Our project is open source on github4

Three-Dimensional Numerical Analysis of Blast-Induced Damage Characteristics of the Intact and Jointed Rockmass

This article reports numerical results investigating the damage evolution and spatial distribution characteristics of intact and jointed rockmass subjected to blast loading. The behaviors of rock material are described by the Holmquist- Johnson-Cook (HJC) constitutive model incorporated in the finite element software LS-DYNA. Results indicate that the damage distribution shows a reverse S-shape attenuation with the increase of the distance from borehole, and a better goodness of fit with the Logistic function is observed. In the single-hole blasting of jointed rockmass, there are two types of regions around the intersection of borehole and joint in which the damage degree is quite different. The crushing damage develops in a Ψ-shape path along the joint. In the radial direction, the crushing damage and cracking damage of rock show different distribution forms with the increase of joint dip angle. As for the double-hole blasting, due to the superposition of the blast waves, the damage degree in the region between the two boreholes of intact rockmass is significantly large. For jointed rockmass, the joint has local enhancement or inhibition effect on the blast damage in the region between the two boreholes

Key Process Protection of High Dimensional Process Data in Complex Production

In order to solve the problem of locating and protecting key processes and detecting outliers efficiently in complex industrial processes. An anomaly detection system which is based on the two-layer model fusion frame is designed in this paper. The key process is located by using the random forest model firstly, then the process data feature selection, dimension reduction and noise reduction are processed. Finally, the validity of the model is verified by simulation experiments. It is shown that this method can effectively reduce the prediction accuracy variance and improve the generalization ability of the traditional anomaly detection model from the experimental results

Localization Based Evolutionary Routing (LOBER) for Efficient Aggregation in Wireless Multimedia Sensor Networks

Efficient aggregation in wireless sensor nodes helps reduce network traffic and reduce energy consumption. The objective of this work Localization Based Evolutionary Routing (LOBER) is to achieve global optimization for aggregation and WMSN lifetime. Improved localization is achieved by a novel Centroid Based Octant Localization (CBOL) technique considering an arbitrary hexagonal region. Geometric principles of hexagon are used to locate the unknown nodes in the centroid positions of partitioned regions. Flower pollination algorithm, a meta heuristic evolutionary algorithm that is extensively applied in solving real life, complex and nonlinear optimization problems in engineering and industry is modified as Enhanced Flower Pollination Algorithm (EFPA) to fit into WMSN and enhance routing mechanism and ensure efficiency in data aggregation. The system is simulated using MATLAB and found to have a considerable improvement in the optimization process

Improved Enhanced Dbtma with Contention-Aware Admission Control to Improve the Network Performance in Manets

DBTMA relies entirely on RTS/CTS dialogue for un-collided transmission of data. The purpose is to improve the QoS at MAC layer by developing it over 802.11e standard. However, DBTMA does not guarantee real-time constraints without efficient method for controlling the network loads. The main challenges in MANETs include prediction of the available bandwidth, establishing communication with neighboring nodes and predicting the consumption of bandwidth flow. These challenges are provided with solutions using Contention-Aware Admission Control (CACP) protocol. In this paper, the EDBTMA protocol is combined with CACP protocol that introduces bandwidth calculation using admission control strategy. The calculation includes certain metrics like: admission control and bandwidth consumption. To compute the bandwidth of channel, bandwidth utilization and traffic priority is distinguished through dual busy tone is proposed. This operates distinctly on its own packet transmission operation. This CACP mechanism defends the conventional traffic flows from new nodes and based on the measured status information of the channel, it QoS of the admitted flows is maintained. This ensures maximum amount of bandwidth flows accommodated by resources and determines the resources in a system meet the new flow requirements while maintaining existing bandwidth flow levels

Locating Steganalysis of LSB Matching Based on Spatial and Wavelet Filter Fusion

For the case of that only a single stego image of LSB (Least Significant Bit) matching steganography is available, the existing steganalysis algorithms cannot effectively locate the modified pixels. Therefore, an algorithm is proposed to locate the modified pixels of LSB matching based on spatial and wavelet filter fusion. Firstly, the validity of using the residuals obtained by spatial and wavelet filtering to locate the modified pixels of LSB matching is analyzed. It is pointed out that both of these two kinds of residuals can be used to identify the modified pixels of LSB matching with success rate higher than that of randomly guessing. Then, a method is proposed to measure the correlation between the results of two locating algorithms. Statistical results show that there are low correlations between the locating results of spatial filter based algorithm and wavelet filter based algorithm. Then these two kinds of residuals are fused by the voting method to improve the locating performance. The experimental results show that the proposed fusion algorithm can effectively improve the locating accuracy for the modified pixels of LSB matching

Numerical Simulation and Optimization of a Mid-Temperature Heat Pipe Exchanger

In this paper, we take the mid-temperature gravity heat pipe exchanger as the research object, simulate the fluid flow field, temperature field and the working state of heat pipe in the heat exchanger by Fluent software. The effects of different operating parameters and fin parameters on the heat transfer performance of heat exchangers are studied. The results show that the heat transfer performance of the mid-temperature gravity heat pipe exchanger is the best when the fin spacing is between 5 mm and 6 mm, the height of the heat pipe is between 12 mm and 13 mm, and the inlet velocity of the fluid is between 2.5 m/s to 3 m/s

Trichoderma spp. and Bacillus spp. as growth promoters in maize (Zea mays L.)

Microbes that are beneficial to plants are used to enhance the crop growth, yield and are alternatives to chemical fertilizers. Trichoderma and Bacillus are the predominant plant growth-promoting fungi and bacteria. The objective of this study was select, characterize, and evaluate isolates of Trichoderma spp. and Bacillus spp. native from the northern region of Sinaloa, Mexico, and assess their effect on growth promotion in maize (Zea mays L.). In greenhouse conditions, four Trichoderma isolates and twenty Bacillus isolates, as well as two controls, were tested in a completely randomized design with three replicates. We selected the two best strains of Trichoderma and Bacillus: TB = Trichoderma asperellum, TF = Trichoderma virens, B14 = Bacillus cereus sensu lato and B17 = Bacillus cereus, which were evaluated in the field in a completely randomized blocks in factorial arrangement design with three replicates applying different rates of nitrogen fertilizer (0, 150 kg N/ha, and 300 kg N/ha). Treatments 5 (B17 = B. cereus) and 11 (TF = T. virens) both fertilized with 150 kg N/ha showed similar yields and they did not reveal significant differences from the treatments fertilized with 300 kg N/ha. This indicated that treatment 5 (B17= B. cereus with 150 kg N/ha) and treatment 11 (TF= T. virens with 150 kg N/ha) were efficient as growth promoters, by not showing significant differences in root volume and dry weight of foliage. The results indicated a reduction of 50% in the rate of nitrogen to fertilizer required for maize (Zea mays L.) crops. These microorganisms Trichoderma and Bacillus could be an alternative to reduce the use of chemical fertilizers in maize

Influences of ascorbic acid and gibberellic acid in alleviating effects of salinity in Petunia under in vitro

Salinity is one of the abiotic stresses that limits the growth and productivity of many crops. A possible survival strategy for plant under saline conditions is to use compounds that could minimize the harmful effects of salt stress on the plant development. The objective of the presented study was to investigate the effect of exogenous ascorbic acid (ASA) with or without gibberellic acid (GA3) on key growth and biochemical parameters in two petunia cultivars ‘Prism Rose’ and ‘Prism White’ under saline (150 mM NaCl) and non-saline in vitro condition. Nodal cutting with an axillary buds were used as explants. Application of 1 mM ascorbic acid with or without 0.05 mM gibberellic acid into the MS medium stimulated the length of shoots and the number of new shoots of ‘Prism Rose’; whereas, it decreased the root length and the number of roots of both ‘Prism Rose’ and ‘Prism White’ under non-saline condition. The addition of ascorbic acid with or without gibberellic acid into the MS medium under saline condition, increased the length of plants and the number of new shoots, but did not affect their root number and length. NaCl treatments increased the proline content and lipid peroxidation which was indicated by the accumulation of malondialdehyde (MDA). The study revealed a correlation between chlorophylls a and b content and the leaf pigmentation intensity – parameter a*. Addition of 1 mM ascorbic acid with 0.05 mM gibberellic acid into the MS medium plays a protective role in salinity tolerance by improving the shoot growth and the development as well as increasing the activities of the antioxidant enzymes and other antioxidant substances

A Novel Image Categorization Strategy Based on Salp Swarm Algorithm to Enhance Efficiency of MRI Images

The main target of this paper is presentation of an efficient method for MRI images classification so that it can be used to diagnose patients and non-patients. Image classification is one of the prominent subset topics of machine learning and data mining that the most important image technique is the auto-categorization of images. MRI images with high resolution and appropriate accuracy allow physicians to decide on the diagnosis of various diseases and treat them. The auto categorization of MRI images toward diagnosing brain diseases has been being used to accurately diagnose hospitals, clinics, physicians and medical research centers. In this paper, an effective method is proposed for categorizing MRI images, which emphasizes the classification stage. In this method, images have been firstly collected and tagged, and then the discrete wavelet transform method has been implemented to extract the relevant properties. All the ready features in a matrix will be subsequently held, and PCA method has been applied to reduce the features dimension. Furthermore, a new model using support vector machine classifier with radial basis function kernel i.e. SVM+RBF has been performed. The SVM Algorithm must bimanually initialized, while, these values have been automatically entered into the SVM classifier by Salp Swarm Algorithm (SSA): Due to high performance of SSA in fast and accurate solution of nonlinear problem as compared to other optimization algorithms, it has been applied to optimally solve the designed problem. Finally, after applying the optimal parameters and SVM classification training, the test data has been utilized and evaluated. The results have transparently suggested the effectiveness of the proposed method in the Accuracy criteria with 0.9833, the Sensitivity with 1, Specificity with 0.9818 and Error with 0.0167 in best iteration as compared to the conventional SVM method