In this paper, we aim to explore the possibility of the Transformer model in detecting the spam Short Message Service (SMS) messages by proposing a modified Transformer model that is designed for detecting SMS spam messages. The evaluation of our proposed spam Transformer is performed on SMS Spam Collection v.1 dataset and UtkMl’s Twitter Spam Detection Competition dataset, with the benchmark of multiple established machine learning classifiers and state-of-the-art SMS spam detection approaches. In comparison to all other candidates, our experiments on SMS spam detection show that the proposed modified spam Transformer has the optimal results on the accuracy, recall, and F1-Score with the values of 98.92%, 0.9451, and 0.9613, respectively. Besides, the proposed model also achieves good performance on the UtkMl’s Twitter dataset, which indicates a promising possibility of adapting the model to other similar problems.

The detection of periodicity in a time series is considered a challenge in many research areas. The difficulty of period length extraction involves the varying noise levels among working environments. A system that performs well in one environment may not be accurate in another. Different methods, including deep neural networks, have been proposed across many applications to find suitable solutions to the period length extraction problem. This paper proposes a convolutional neural network (CNN) based period classification algorithm, named PCA, to detect the dataset periods. In particular, assuming that a data stream contains periodical features, the PCA utilizes historical labeled data as training material and classifies new instances accordingly based on their periods. Its performance has been tested on both synthetic and real-world periodic time series data (PTSD) with very encouraging results. In particular, We have observed that the PCA is capable of achieving 100% accuracy in the case of low noise PTSD. Even the training of the PCA is not economical if the data do not contain much noise, it still demonstrates high performance on both synthetic and real-world datasets. Besides, we have shown that our new algorithm can capture the relationship between the shape of the waves and the target period, which is significantly different from the classical methods that mainly focus on the wave’s amplitude.

Full quantum capability devices can provide secure communications, but they are challenging to make portable given the current technology. Besides, classical portable devices are unable to construct communication channels resistant to quantum computers. Hence, communication security on portable devices cannot be guaranteed. Semi-Quantum Communication (SQC) attempts to break the quandary by lowering the receiver’s required quantum capability so that secure communications can be implemented on a portable device. However, all SQC protocols have low qubit efficiency and complex hardware implementations. The protocols involving quantum entanglement require linear Entanglement Preservation Time (EPT) and linear quregister size. In this paper, we propose two new keyless SQC protocols that address the aforementioned weaknesses. They are named Economic Keyless Semi-Quantum Point-to-point Communication (EKSQPC) and Rate Estimation EKSQPC (REKSQPC). They achieve theoretically constant minimal EPT and quregister size, regardless of message length. We show that the new protocols, with low overhead, can detect Measure and Replay Attacks (MRA). REKSQDC is tolerant to transmission impairments and environmental perturbations. The protocols are based on a new quantum message transmission operation termed Tele-Fetch. Like QKD, their strength depends on physical principles rather than mathematical complexity.

Network structures, consisting of nodes and edges, have applications in almost all subjects. A set of nodes is called a community if the nodes have strong interrelations. Industries (including cell phone carriers and online social media companies) need community structures to allocate network resources and provide proper and accurate services. However, most detection algorithms are derived independently, which is arduous and even unnecessary. Although recent research shows that a general detection method that serves all purposes does not exist, we believe that there is some general procedure of deriving detection algorithms. In this paper, we represent such a general scheme. We mainly focus on two types of networks: transmission networks and similarity networks. We reduce them to a unified graph model, based on which we propose a method to define and detect community structures. Finally, we also give a demonstration to show how our design scheme works.

The demand and supply problems are encountered widely in almost all fields. While the users hope to get their demand satisfied always, the suppliers do not want to produce superfluous supplies. One way to tackle this problem is to have a good prediction on the future demand. Many forecasting algorithms have been proposed by using time series and extrapolation techniques. However, most of the algorithms are based on the complex theories and consume potentially large amount computing resources. In this paper, we propose a new algorithm to predict the demand by applying classical learning automata techniques. The algorithm uses more concise strategies and consumes considerably less computing resources.

Network structures, consisting of nodes and edges, have applications in almost all subjects. A set of nodes is called a community if the nodes have strong interrelations. Industries (including cell phone carriers and online social media companies) need community structures to allocate network resources and provide proper and accurate services. However, all the current detection algorithms are motivated by the practical problems, whose applicabilities in other fields are open to question. Thence, for a new community problem, researchers need to derive algorithms ad hoc, which is arduous and even unnecessary. In this paper, we represent a general procedure to find community structures in practice. We mainly focus on two typical types of networks: transmission networks and similarity networks. We reduce them to a unified graph model, based on which we propose a general method to define and detect communities. Readers can specialize our general algorithm to accommodate their problems. In the end, we also give a demonstration to show how the algorithm works.