Energy-efficient workload allocation in edge-cloud fiber-wireless networks.

In order to realize the green computing of the edge-cloud fiber-wireless networks, the cooperation between the edge servers and the cloud servers is particularly important to reduce the network energy consumption. Therefore, this paper proposes an energy-efficient workload allocation (EEWA) scheme to improve the energy efficiency by using the architecture of edge-cloud fiber-wireless networks. The feasibility of the proposed EEWA scheme was verified on our SDN testbed. We also do the simulation to obtain the optimal results for a given set of task requests. Simulation results show that our proposed EEWA scheme greatly reduces the blocking probability and the average energy consumption of task requests in edge-cloud fiber-wireless networks.

DeeProBot: a hybrid deep neural network model for social bot detection based on user profile data.

Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one's opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to classify Twitter accounts as either 'human' or 'bot.' We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the framework 'DeeProBot,' which stands for Deep Profile-based Bot detection framework. The raw text from the description field of the Twitter account is also considered a feature for training the model by embedding the raw text using pre-trained Global Vectors (GLoVe) for word representation. Using only the user profile-based features considerably reduces the feature engineering overhead compared with that of user timeline-based features like user tweets and retweets. DeeProBot handles mixed types of features including numerical, binary, and text data, making the model hybrid. The network is designed with long short-term memory (LSTM) units and dense layers to accept and process the mixed input types. The proposed model is evaluated on a collection of publicly available labeled datasets. We have designed the model to make it generalizable across different datasets. The model is evaluated using two ways: testing on a hold-out set of the same dataset; and training with one dataset and testing with a different dataset. With these experiments, the proposed model achieved AUC as high as 0.97 with a selected set of features.

Engineering a Ga-Gradient by One-Step Sputtering to Achieve Over 15% Efficiency of Cu(In,Ga)Se2 Flexible Solar Cells without Post-selenization.

A one-step sputtering process using a quaternary target has been demonstrated to be a simple route to form Cu(In,Ga)Se2 (CIGSe) absorber without post-selenization; however, the lack of a Ga-grading structure in the CIGSe absorber confines its efficiency. Here, we demonstrate a one-step cosputtering process to control the Ga profile in the CIGSe absorber on flexible stainless steel substrates. Special attention was paid to the formation of second phases and their effects on the cell performance. Although the normal Ga-grading and efficiency enhancement could be achieved by cosputtering of CIGSe and Ga2Se3 targets, high-energy ion bombardment during the sputtering process might cause the decomposition of the Ga2Se3 target, leading to the formation of Ga2O3 in the CIGSe absorber, which gradually degraded the device performance. We replaced the Ga2Se3 target with a stoichiometric CuGaSe2 target for cosputtering, which can further enhance the cell efficiency due to the elimination of Ga2O3. However, when the Ga content at the back side of CIGSe is further increased by raising the deposition power of the CuGaSe2 target, the phase separation of CuGaSe2 may take place, resulting in the formation of Cu2-XSe and CuGaSe2 at the back side of the CIGSe absorber; therefore, the recombination at the back side is increased. By cosputtering a CIGSe target with a Cu-deficient CuGaSe2 target, we can suppress the formation of second phases and achieve designable normal grading, leading to the highest efficiency of 15.63% without post-selenization on flexible substrates.