Adversarial Activity Detection and Prediction Using Behavioral Biometrics

Behavioral biometrics can be used in different security applications like authentication, identification, etc. One of the trending applications is predicting future activities of people and guessing whether they will engage in malicious activities in the future. In this research, we study the possibility of predicting future activities and propose novel methods for near-future activity prediction. First, we study gait signals captured using smartphone accelerometer sensor and build a model to predict a future gait signal. Activity recognition using body movements captured from mobile phone sensors has been a major point of interest in recent research. Data that is being continuously read from mobile sensors can be used to recognize user activity. We propose a model for predicting human body movements based on the previous activity that has been read from sensors and continuously updating our prediction as new data becomes available. Our results show that our model can predict the future movement signal with a high accuracy that can contribute to several applications in the area. Second, we study keystroke acoustics and build a model for predicting future activities of the users by recording their keystrokes audio. Using keystroke acoustics to predict typed text has significant advantages, such as being recorded covertly from a distance and requiring no physical access to the computer system. Recently, some studies have been done on keystroke acoustics, however, to the best of our knowledge none have used them to predict adversarial activities. On a dataset of two million keystrokes consisting of seven adversarial and one benign activity, we use a signal processing approach to extract keystrokes from the audio and a clustering method to recover the typed letters followed by a text recovery module to regenerate the typed words. Furthermore, we use a neural network model to classify the benign and adversarial activities and achieve significant results: (1) we extract individual keystroke sounds from the raw audio with 91% accuracy and recover words from audio recordings in a noisy environment with 71% average top-10 accuracy. (2) We classify adversarial activities with 93% to 98% average accuracy under different operating scenarios. Third, we study the correlation between the personality traits of users with their keystroke and mouse dynamics. Even with the availability of multiple interfaces, such as voice, touch, etc., keyboard and mouse remain the primary interfaces to a computer. Any insights on the relation between keyboard and mouse dynamics with the personality type of the users can provide foundations for various applications, such as advertisement, social media, etc. We use a dataset of keystroke and mouse dynamics collected from 104 users together with their responses to two personality tests to analyze how their interaction with the computer relates to their personality. Our findings show that there are considerable trends and patterns in keystroke and mouse dynamics that are correlated with each personality type

Effects of sugar beet cultivar on development and reproductive capacity of Aphis fabae

Abstract Black been aphid, Aphis fabae Scopoli (Homoptera Aphididae) is recognized as a serious pest of sugar beet with worldwide distribution. Development and fecundity rates of this aphid were evaluated on six commonly growing cultivars under laboratory conditions in Ardabil County, Iran. The results obviously clarified significant differences in biology and life history characteristics of A. fabae reared on different sugar beet cultivars. The shortest developmental time for the immature stages was observed to be 11.32 days on 'Polyrave' and the longest 13.23 days on '7233'. There was the highest fecundity (14.33, nymphs/female) of A. fabae on 'Polyrave' and the lowest (7.32, nymphs/female) on '7233' cultivar. The r m values of the aphid ranged from 0.1336 on '7233' to 0.2202 (nymphs/female/day) on 'Polyrave'. In general, Jackknife estimates of this aphid population parameters on cultivars examined showed the highest development and fecundity rates on 'Polyrave' and the lowest on '7233' cultivar

Impact of Ssuper Absorbent Polymer and Irrigation Management on Seed and Essential Oil Yields of Cumin

Two field and laboratory experiments were conducted to investigate the effects of superabsorbent polymer (SAP) and irrigation management on seed and essential oil yields of Cumin, as well as the impact of water quality on water holding capacity of SAP. Salinity had a negative effect on the amount of water absorbed by SAP (335 and 59 g H2O per g SAP, for distilled water and solution of 0.5% NaCl, respectively). SAP application (30 kg.ha-1) along with three times irrigation at sowing, flowering and seed filling stages increased the amounts of seed and essential oil yields by 2.79 and 3.05 times, compared to control. Positive effects of SAP were related to enhancement of soil water holding capacity (120 gr irrigation water per gr SAP), leaf area duration (one week) and subsequently grain filling period

Harnessing the Power of CAR-NK Cells: A Promising Off-the-Shelf Therapeutic Strategy for CD38-Positive Malignancies

Background: CD38 is highly expressed on multiple myeloma (MM) cells and has been successfully targeted by different target therapy methods. This molecule is a critical prognostic marker in both diffuse large B-cell lymphoma and chronic lymphocytic leukemia.Objective: We have designed and generated an anti-CD38 CAR-NK cell applying NK 92 cell line. The approach has potential application as an off-the-shelf strategy for treatment of CD38 positive malignancies.Methods: A second generation of anti-CD38 CAR-NK cell was designed and generated, and their efficacy against CD38-positive cell lines was assessed in vitro. The PE-Annexin V and 7-AAD methods were used to determine the percentage of apoptotic target cells. Flow cytometry was used to measure IFN-γ, Perforin, and Granzyme-B production following intracellular staining. Using in silico analyses, the binding capacity and interaction interface were evaluated.Results: Using Lentivirus, cells were transduced with anti-CD38 construct and were expanded. The expression of anti-CD38 CAR on the surface of NK 92 cells was approximately 25%. As we expected from in silico analysis, our designed CD38-chimeric antigen receptor was bound appropriately to the CD38 protein. NK 92 cells that transduced with the CD38 chimeric antigen receptor, generated significantly more IFN-γ, perforin, and granzyme than Mock cells, and successfully lysed Daudi and Jurkat malignant cells in a CD38-dependent manner.Conclusion: The in vitro findings indicated that the anti-CD38 CAR-NK cells have the potential to be used as an off-the-shelf therapeutic strategy against CD38-positive malignancies. It is recommended that the present engineered NK cells undergo additional preclinical investigations before they can be considered for subsequent clinical trial studies

Enhanced characterization of beta cell mass in a Tg(Pdx1-GFP) mouse model

Introduction: Measurement of pancreatic beta cell mass in animal models is a common assay in diabetes researches. Novel whole-organ clearance methods in conjunction with transgenic mouse models hold tremendous promise to improve beta cell mass measurement methods. Here, we proposed a refined method to estimate the beta cell mass using a new transgenic Tg(Pdx1-GFP) mouse model and a recently developed free-of-acrylamide clearing tissue (FACT) protocol. Methods: First, we generated and evaluated a Tg(Pdx1-GFP) transgenic mouse model. Using the FACT protocol in our model, we could quantify the beta cell mass and alloxan-induced beta cell destruction in whole pancreas specimens. Results: Compiled fluorescent images of pancreas resulted in enhanced beta cell mass characterization in FACT-cleared sections (2928869±120215 AU) compared to No-FACT cleared sections (1292372±325632 AU). Additionally, the total number of detected islets with this method was significantly higher than the other clearance methods (155.7 and 109, respectively). Using this method, we showed green fluorescent protein (GFP) expression confined to beta cells in Tg(Pdx1-GFP) transgenic. This enhanced GFP expression enabled us to accurately measure beta cell loss in a beta cell destruction model. The results suggest that our proposed method can be used as a simple, and rapid assay for beta cell mass measurement in islet biology and diabetes studies. Conclusion: The Tg(Pdx1-GFP) transgenic mouse in conjunction with the FACT protocol can enhance large-scale screening studies in the field of diabetes

Analysis of Key Barriers to theUse of the Internet of Things in Iranian Smart Cities (Structural Analysis Method)

Today, many countries are institutionalizing the concept of smart city and implementing IoT programs that support smart city components, relying on the extensive IT infrastructure to improve the sustainability and quality of life needed by their communities. But using the Internet of Things to develop a smart city faces several challenges. Accordingly, the purpose of this study is to identify the main challenges of the Internet of Things and to understand the relationship between these challenges to support the development of smart cities in Iran. Initially, 14 major challenges were extracted based on domestic and foreign literature. Then, using the SWARA technique, the challenges were prioritized based on summarizing the views of seven relevant experts in the field of smart cities in Iran. In the next step, the underlying interactions between the identification challenges and their importance were determined using interpretive structural modeling (ISM). Then, MICMAC method was used to confirm the results. According to the findings, the challenges in the interpretive structural method were leveled at six levels. Then, to test the results of this stage, MICMAC method was used.The results of this stage showed that the lack of policies, perspectives and regulatory guidelines, high training, operational and maintenance costs, inequality (social), lack of transparency and responsibility, lack of technical knowledge among Planners, lack of technology infrastructure and lack of intelligence were identified as influential and key variables of the study

Modeling Pyramidal Absorbers Using the Fourier Modal Method and the Mode Matching Technique

The problem of plane wave diffraction from the periodic structure of an infinite PEC wall lined with pyramidal absorbers is considered. A hybrid method based on the R-matrix Fourier modal method (RFMM) and the mode matching (MM) of fields is used for the efficient and robust analysis of this class of absorbers. The hybrid method benefits from the discrete spectrum of the periodic structure in transverse directions and consequently avoids spatial discretization along the periodicity axes. This leads to considerable reduction in the number of unknown coefficients. Furthermore, the method is capable of considering dispersive and inhomogeneous materials which are frequently used in the absorber industry. Several examples are outlined, simulated, and measured to show the efficiency and accuracy of the method. The versatility and flexibility of the hybrid RFMM-MM technique makes it suitable for the optimal design of pyramidal absorbers to achieve further improvements in their performances

Application of Bayesian networks in corrective maintenance safety

AbstractThe emphasis of this exploratory paper is on some maintenance related accidents occurred during previous decades and the aim is to develop a model for preventing such accidents by understanding the major causes and the influencing factors among them. The model is intended to explain the affecting factors in a fatal accident and how they are related to one another. To fulfil the aim of study, a Bayesian network model was developed using the GeNIe 2.0 software and further on the relations between different factors were assigned. The model was developed based on a case study of Tosco Avon refinery accident in Martinez, California occurred on February 23rd, 1999 where four workers were killed in a fiery accident. The proposed model could predict the final result of the execution of a corrective maintenance process to make a safer decision. Since the model was developed based on Tosco Avon refinery accident case, validation has been carried out regarding some other accidents. The results showed that the proposed model is a suitable tool to predict the outcomes of an action in corrective maintenance. In addition the model can be adjusted to different applications

Application of Bayesian networks in corrective maintenance safety

Abstract The emphasis of this exploratory paper is on some maintenance related accidents occurred during previous decades and the aim is to develop a model for preventing such accidents by understanding the major causes and the influencing factors among them. The model is intended to explain the affecting factors in a fatal accident and how they are related to one another. To fulfil the aim of study, a Bayesian network model was developed using the GeNIe 2.0 software and further on the relations between different factors were assigned. The model was developed based on a case study of Tosco Avon refinery accident in Martinez, California occurred on February 23rd, 1999 where four workers were killed in a fiery accident. The proposed model could predict the final result of the execution of a corrective maintenance process to make a safer decision. Since the model was developed based on Tosco Avon refinery accident case, validation has been carried out regarding some other accidents. The results showed that the proposed model is a suitable tool to predict the outcomes of an action in corrective maintenance. In addition the model can be adjusted to different applications