Ranking entities on the basis of users’ opinions

Online opinions are one of the most important sources of information on which users base their purchasing decisions. Unfortunately, the large quantity of opinions makes it difficult for an individual to consume in a reasonable amount of time. Unlike standard information retrieval problems, the task here is to retrieve entities whose relevance is dependent upon other people’s opinions regarding the entities and how well those sentiments match the user’s own preferences. We propose novel techniques that incorporate aspect subjectivity measures into weighting the relevance of opinions of entities based on a user’s query keywords. We calculate these weights using sentiment polarity of terms found proximity close to keywords in opinion text. We have implemented our techniques, and we show that these improve the overall effectiveness of the baseline retrieval task. Our results indicate that on entities with long opinions our techniques can perform as good as state-of-the-art query expansion approaches.     

Bond Slip Analysis of Fiber-Reinforced Polymer-Strengthened Beams

The second order differential equation of interface shear is formulated for fiber-reinforced polymer-strengthened beams using beam theory with a shear deformable adhesive layer. The solution of the boundary value problem is obtained in closed form and is used to derive deflection expressions for different loading conditions. The solution is also extended to analyze partially plated beams. The results converge to the extreme cases of very poorly and perfectly bonded plates and they help identify values of the adhesive shear modulus for effective stiffening. Furthermore, the solution of partially plated beams aids in defining anchorage lengths needed to develop the full or the highest possible composite action at midspan.     

Investigations of field-evaporated end forms in voltage- and laser-pulsed atom probe tomography

Atom Probe Tomography (APT) consists of analyzing a needle-shaped specimen on an atom-by-atom basis. In recent years, instruments have become commercially available, enabling the sequential analysis of the same specimen in both laser- and voltage-pulsed modes. In this contribution, a comparison of field evaporated end-forms as a function of the voltage and laser power is presented for silicon. Electron microscopy is utilized for visual inspection of the final tip end-forms. The field of evaporation for silicon is calculated based on these radius measurements for voltage and laser pulsing. Electron microscopy and analysis of the atom probe data show that the specimen end-forms for both pulsing modes can be different. We have observed two effects on the shape of a field-ion emitter when irradiated by a focused laser beam. One is a change in the 3-dimensional topology of the emitter due to different crystallographic orientations. Secondly, exposure to focused laser beam from one side may lead to a non-hemispherical tip shape especially when reasonably high laser energy is utilized. For comparison purposes to the laser mode, the voltage pulse evaporated tip end form is also analyzed for different specimen temperatures. Consequently, evaporation fields are calculated for different temperatures and laser conditions for silicon.     

Enhanced regression testing technique for agile software development and continuous integration strategies

Software Quality Journal - To survive in competitive marketplaces, most organizations have adopted agile methodologies to facilitate continuous integration and faster application delivery and rely...     

Correction to: AnonSURP: an anonymous and secure ultralightweight RFID protocol for deployment in internet of vehicles systems

The Journal of Supercomputing -     

Combining pre-retrieval query quality predictors using genetic programming

Predicting the effectiveness of queries plays an important role in information retrieval. In recent years, a number of methods are proposed for this task, however, there has been little work done on combining multiple predictors. Previous studies on combining multiple predictors rely on non-backtracking based machine learning methods. These studies show minor improvement over single predictors due to the limitation of non-backtracking. This paper discusses work on using machine learning to automatically generate an effective predictors’ combination for query performance prediction. This task is referred to as—learning to predict for query performance prediction in the field. In this paper, a learning method, PredGP, is presented to address this task. PredGP employs genetic programming to learn a predictor by combining various pre-retrieval predictors. The proposed method is evaluated using the TREC Chemical Prior-Art Retrieval Task dataset and found to be significantly better than single predictors.     

Exploring the next neighbourhood relationship in amorphous alloys utilizing atom probe tomography

A new algorithm is developed to explore the next neighbourhood atomic vicinity from the analysed data obtained using the tomographic atom probe (TAP) technique. The presented algorithm allows to calculate the atomic distances among different next neighbours of different elements as applied to bulk amorphous alloys. The results obtained for Pd55 Cu23 P22 bulk amorphous alloys show reasonable consistency to already available data from different diffraction techniques. The Pd-Pd atoms have highest probability to be a next neighbour than others. The established view that P is not a direct next neighbour to each other is also manifested from these results. Normalizing the distances of the next neighbours to the first neighbour distance in this particular amorphous system possesses a definite order for all elemental correlations. Furthermore, the algorithm was processed for different critical reconstruction parameters to explore the corresponding effect on the distance distribution of next neighbouring atoms. The minor changes in the product of the geometric factor and the evaporation field of the sample does not make any egregious difference on the next neighbourhood evaluation (NNE).     

Investigation on multiferroic properties of BiFeO3 ceramics

BiFeO₃ polycrystalline ceramics was prepared by solid-state reaction method and its structural, optical and magnetic properties were investigated. BiFeO₃ was synthesized in a wide range of temperature (825–880 °C) and a well crystalline phase was obtained at a sintering temperature of 870 °C. X-ray diffraction patterns of the samples were recorded and analyzed for the confirmation of crystal structure and the determination of the lattice parameters. The average grain size of the samples was found to be between 1–2 μm. The determined value of direct bandgap of BiFeO₃ ceramics was found to be 2.72 eV. The linear behavior of M-H curve at room temperature confirmed antiferromagetic properties of the BiFeO₃ (BFO). S shaped M-H curve was obtained at a temperature of 5 K. In the whole temperature measurement range (5–300 K) of M-T, no anomalies were observed due to high Curie temperature and Neel temperature of the BiFeO₃.     

Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

Digital surveillance systems are ubiquitous and continuously generate massive amounts of data, and manual monitoring is required in order to recognise human activities in public areas. Intelligent surveillance systems that can automatically ide.pngy normal and abnormal activities are highly desirable, as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring. This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system. The proposed system addresses the limitations of existing manual monitoring surveillance systems using a three-step framework. In the first step, the salient frames are selected from the online video stream using a frame differencing method. A lightweight 3D convolutional neural network (3DCNN) architecture is applied to extract spatio-temporal features from the salient frames in the second step. Finally, the probabilities predicted by the 3DCNN network and the metadata of the cameras are processed using a linear threshold gate sigmoid mechanism to control the priority of the camera. The proposed system performs well compared to state-of-the-art violent activity recognition methods in terms of efficient camera prioritisation in large-scale surveillance networks. Comprehensive experiments and an evaluation of activity recognition and camera prioritisation showed that our approach achieved an accuracy of 98% with an F1-score of 0.97 on the Hockey Fight dataset, and an accuracy of 99% with an F1-score of 0.98 on the Violent Crowd dataset.