A discrete hidden Markov model for SMS spam detection

Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naive Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets

Non-linear dynamic data reconciliation for industrial processes

This paper investigates and improves a technique known as Nonlinear Dynamic Data Reconciliation (NDDR) for a real industrial process. NDDRS is a technique for data reconciliation that requires an objective function to be minimised subject to both algebraic and differential, equality and inequality constraints. These constraints are obtained from the mathematical description of the process and ensure that the measurement data can be optimised to conform as closely as possible to the true behaviour of the process. One of the difficulties of using the original NDDR is that a rigorous process dynamic model is required as a constraint. Unfortunately it is very hard to establish a rigorous dynamic model for a complex industrial process, particularly for data reconciliation purpose. A transfer function matrix model has been introduced in this new NDDR method. Therefore the rigorous dynamic model is avoided. The real industrial data from FCCU is used to illustrate the efficiency of the new NDDR method

Reference model based maintenance of control system performance for industrial processes

In the last decade, fault tolerant controls (FTC) have enjoyed tremendous success to effectively accommodate defects in sensors, actuators, or plants. However, little of them considered what should be done once a control system performance is degraded during the operation. The aim of this paper is to maintain the performance of a control system at an acceptable level based on a pre-defined reference model. A maintenance approach is proposed and experimented in this paper. The method is to insert a compensator into the faulty control system and make the compensator and the faulty open loop system working together to track the pre-defined reference model. The proposed method is illustrated by reference to a mini process rig and shows the potential to industrial processes

Reference Model Based Maintenance of Control System Performance for Industrial Processes

In the last decade, fault tolerant controls (FTC) have enjoyed tremendous success to effectively accommodate defects in sensors, actuators, or plants. However, little of them considered what should be done once a control system performance is degraded during the operation. The aim of this paper is to maintain the performance of a control system at an acceptable level based on a pre-defined reference model. A maintenance approach is proposed and experimented in this paper. The method is to insert a compensator into the faulty control system and make the compensator and the faulty open loop system working together to track the pre-defined reference model. The proposed method is illustrated by reference to a mini process rig and shows the potential to industrial processes

Non-linear Dynamic Data Reconciliation For Industrial Processes

This paper investigates and improves a technique known as Nonlinear Dynamic Data Reconciliation (NDDR) for a real industrial process. NDDRS is a technique for data reconciliation that requires an objective function to be minimised subject to both algebraic and differential, equality and inequality constraints. These constraints are obtained from the mathematical description of the process and ensure that the measurement data can be optimised to conform as closely as possible to the true behaviour of the process. One of the difficulties of using the original NDDR is that a rigorous process dynamic model is required as a constraint. Unfortunately it is very hard to establish a rigorous dynamic model for a complex industrial process, particularly for data reconciliation purpose. A transfer function matrix model has been introduced in this new NDDR method. Therefore the rigorous dynamic model is avoided. The real industrial data from FCCU is used to illustrate the efficiency of the new NDDR method

A facile chemical conversion synthesis of Sb2S3 nanotubes and the visible light-driven photocatalytic activities

We report a simple chemical conversion and cation exchange technique to realize the synthesis of Sb2S3 nanotubes at a low temperature of 90°C. The successful chemical conversion from ZnS nanotubes to Sb2S3 ones benefits from the large difference in solubility between ZnS and Sb2S3. The as-grown Sb2S3 nanotubes have been transformed from a weak crystallization to a polycrystalline structure via successive annealing. In addition to the detailed structural, morphological, and optical investigation of the yielded Sb2S3 nanotubes before and after annealing, we have shown high photocatalytic activities of Sb2S3 nanotubes for methyl orange degradation under visible light irradiation. This approach offers an effective control of the composition and structure of Sb2S3 nanomaterials, facilitates the production at a relatively low reaction temperature without the need of organics, templates, or crystal seeds, and can be extended to the synthesis of hollow structures with various compositions and shapes for unique properties

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival