Programmable Logic Controllers (PLCs) are an integral component for managing automation processes of smart buildings. PLCs use protocols which make these control systems vulnerable to many common attacks due to which it is possible to create conflicts on certain devices of smart buildings thereby disrupting functionality. In this paper, we propose DEFEASIBLE-PROV, a system for resolving conflicts in the system by detecting the conflict creating sensors and conflict impacted actuators. Our tool is capable of blocking conflict creating rules in the system. Our evaluation results show that our proposed methodology contributes significantly to conflict resolution in the system.

Programmable Logic Controller (PLC) suffers from various types of attacks due to misconfiguration in its logic system. These misconfigurations cannot be found by looking at the logic/rules only. Therefore, we propose a methodology where temporal logic is used to find the misconfigurations in the subsequent rules. Our framework is capable of finding the codes in PLC that are responsible for abnormal behavior in PLC. Having accomplished that, an automated approach to instrument the code. We run our methodology on some real world PLC program applications and found that our approach can eradicate a good number of abnormal behaviors successfully.

The growth of social media is causing the emergence of hate speech. Email extortion and cyberbullying are on the rise in Bangladesh, along with online sexual harassment of women. In order to prevent these crimes, studies on Bengali comments on social media have become progressively important. However, the requisite datasets are scarce for this kind of study. The motive of this research is to create a dataset of Bangla comments from social platforms and develop a classifier model as well as to detect whether the comments are social or anti-social quickly and efficiently. 2000 comments were gathered from Facebook and YouTube, two prominent platforms for social media. In our study, an artificial neural network model like Gated Recurrent Unit (GRU), and supervised machine learning classifiers like Logistic Regression (LR), Random Forest (RF), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVM) were utilized in our study to distinguish between anti-social and socially acceptable comments. Finally, language models such as unigrams, bigrams, and trigrams have been implemented in our research. To the best of our knowledge, there are no studies regarding the anti-social classification in Bangla language. This work will help to prevent anti-social activities in Bangla community.

still show more fistula rates and other complications [2][3][4][5][6].Moreover, there is still debate about the ultimate outcome of incising a urethral plate and the application of TIP where the urethral plate is narrow.There has been concern among surgeons that metal stenosis and urethra-cutaneous (UC) fis-

Crossfire Denial of Service (DDoS) is a new and organized type of attack to make the services of a target organization unavailable. The adversaries, in such attack, adveraremploy BOTs and decoy servers to flood critical links that are used to communicate with the target. In this paper, we propose an optimization framework, OPD (Optimized Packet Distributor), for distributing the packets in the most balanced way after a crossfire DDoS attack gets detected in a network. We formulate the network packet distribution problem as a non-linear link weight function and solved the optimization problem with a very efficient algorithm, Frank Wolf (FW). FW is the most efficient algorithm for solving convex set optimization problem. It achieves network equilibrium (or converges) with very few iterations. OPD will help the commonly used routing algorithm of Software Defined Network (SDN) by providing an optimized number of packets for each link. When a router sends a packet, it will follow the packet distribution proposed by OPD. We simulated a crossfire attack in NS2 simulator. The number of packets traveling in each link is collected for a given time frame from NS2. Based on this packet distribution, OPD proposes desired packet flows for the network. After evaluating the result of OPD, we have found that it can distribute the packets to the links that were less congested during the time of crossfire DDoS attack.

Programmable Logic Controllers are an integral component for managing many different industrial processes (e.g., smart building management, power generation, water and wastewater management, and traffic control systems), and manufacturing and control industries (e.g., oil and natural gas, chemical, pharmaceutical, pulp and paper, food and beverage, automotive, and aerospace). Despite being used widely in many critical infrastructures, PLCs use protocols which make these control systems vulnerable to many common attacks, including man-in-the-middle attacks, denial of service attacks, and memory corruption attacks (e.g., array, stack, and heap overflows, integer overflows, and pointer corruption). In this paper, we propose PLC-PROV, a system for tracking the inputs and outputs of the control system to detect violations in the safety and security policies of the system. We consider a smart building as an example of a PLC-based system and show how PLC-PROV can be applied to ensure that the inputs and outputs are consistent with the intended safety and security policies.

Internet of Things (IoT) has become a common paradigm for different domains such as health care, transportation infrastructure, smart homes, smart shopping, and e-commerce. With its interoperable functionality, it is now possible to connect all domains of IoT together to provide comprehensive services to the users. Because numerous IoT devices can connect and communicate at the same time, there can be events that trigger conflicting actions for an actuator or an environmental feature. This paper provides a formal method approach, IoT Confict Checker (IoTC(2)), to ensure safety of controller and actuators' behavior with respect to conflicts. Any policy violation results in detection of the conflicts. We define the safety policies for controller, actions, and triggering events and implement them in Prolog to prove the logical completeness and soundness. In addition to that, we have implemented the detection policies in Matlab Simulink Environment with its built- in Model Verification blocks. We created a smart home environment in Simulink and showed how the conflicts affect actions and corresponding features. The scalability, efficiency, and accuracy of our method are tested in this simulated environment.

Background: Congenital Diaphragmatic Hernia (CDH) and eventration of Diaphragm(ED) are important causes of respiratory distress in children. CDH and ED are more a medical emergency than a surgical one and after birth, confirmation of the diagnosis should be followed by treatment in a Neonatal Intensive Care Unit (NICU) or Special Care Neonatal Unit (SCANU), Paediatric Intensive Care Unit (PICU) to stabilize the cardiovascular system. Preoperative resuscitation and delayed surgical repair with or without the use of ExtraCorporeal Membrane Oxygenation (ECMO) improves the survivality. Methods: The medical records of all patients with CDH and ED between January 2010 and December 2015 were retrospectively reviewed. Patients’ presentation, management, operative findings and complications were evaluated. Results: 22 patients were diagnosed as CDH or ED. Male to female ratio was 4.5:1. Age range was 2 days to 7 years, median 82.5 days. Eighteen patients were CDH and 4 patients were ED. Six patients had associated malrotation of the gut. 3 patients had Congenital Heart Disease, 1 patient had Gastroschisis and another had Hiatus hernia. Four patients expired. Conclusion: Surgery preceded by a preoperative respiratory resuscitation and stabilization reduces postoperative mortality and increases the survival rate. Outcome was not very unsatisfactory without adequate ventilatory support. Journal of Paediatric Surgeons of Bangladesh (2016) Vol. 7 (1): 3-10

The 21st century has pioneered a technological revolution like no other, where anyone from the government to international corporations to the public can use smart technology and computers to store personal information and conduct large transfers of money or sensitive information. However, along with the widespread use of such technology come the growing risks of cyber attacks and security breaches that can pose disastrous consequences for the system attacked. Over time, humans and other living creatures have developed various natural forms of protection for survival, and these biological instincts and predispositions can be synthetically replicated and applied to cyber security systems to enhance a system's resilience in the face of an attack.

Enterprise networks deploy security devices to control access and limit potential threats. Due to the emergence of zero-day attacks, security device based isolation measures like access denial, trusted communication, and payload inspection are often not adequate for the resilient execution of an organization's mission. Diversity between two hosts in terms of operating systems and services running on these hosts is crucial for limiting the attack propagation. Since different software systems have different vulnerabilities, it is important to have the hosts diversified considering the isolation among the hosts as well as the mission requirements. In this paper, we present a formal model for synthesizing network resiliency configurations. The resiliency design integrates isolation and diversity measures. We take the network topology, resiliency requirements, and business constraints as inputs. Then, our proposed model synthesizes cost-effective resiliency configurations satisfying the constraints. The output of the model provides necessary placements of different security devices in the topology and necessary installments of operating systems and services on the hosts. We demonstrate the execution of the proposed model as well as their scalability using simulated experiments.

Fetus- in- fetu is a rare abnormality secondary to the abnormal embryogenesis in a diamniotic, monochorionic pregnancy. It is a rare pathological condition and fewer than 200 cases have been reported in the literature. We are reporting a case in which a 15 year old girl presented with a painful lump in left upper abdomen. Preoperative imaging, exploration and macroscopic examination of the excised specimen revealed it a case of fetus- in - fetu. This case is unique in terms of age of presentation and mature fetus like external appearance. DOI: http://dx.doi.org/10.3329/jpsb.v2i1.15162 Journal of Paediatric Surgeons of Bangladesh (2011) Vol. 2 (1): 36-39

Biometric face Recognition is a new generation technology for identification and verification. Many techniques have been developed for this purpose in past two decades. A good face recognition technique must require unique, effective and efficient features from face images. Content based image retrieval (CBIR) can extract features that can be useful for face recognition. Recently, Gabor and curvelet texture features have been successfully used in image retrieval research. In this paper, we propose a novel face recognition method that uses texture features obtained by calculating mean and standard deviation of Gabor and curvelet transformed face images. PCA is then applied to the feature vectors instead of entire transformed images which traditional methods do. Using this process, we build four classifiers using mean and standard deviation calculated from Gabor and curvelet transformed face images. For identification purpose, a new matching strategy is proposed that checks goodness of four matching results of the classifiers. As we consider only mean or standard deviation features, the image representation has comparatively lower dimensions. Furthermore, our proposed method does not necessarily require all the input images to be of same resolution. We evaluate the proposed method using ORL and Yale face databases. The recognition results of the experiments show that our approach is significantly better than the conventional methods.

With the development of computer technology, the growing need of large databases emphasizes utmost automation in face recognition system. Labeling a huge number of people is definitely a great overhead for such system. To reduce the burden of manual labeling of faces, in this paper, we propose a PCA based semi-supervised face recognition technique using edge histogram descriptor (EHD) features. It promotes automation of system by achieving good performance toiling at minimum wage. Moreover, we establish that EHD features are not only useful for face representation but also greatly reduce the dimensionality of the representation compared with traditional pixel value representation. Thus, the space and computation complexity decrease in further stages. PCA is then applied on the EHD features instead of raw pixel intensity values of faces which traditional methods do. Using this process, we build a PCA based classifier that can iteratively update itself after classifying unlabeled training instances. We check the performance of the system using three different similarity measures. We also test our system with different levels of noise to simulate practical environment. To evaluate the proposed method, we have used ORL, Yale, Grimace and JAFFE face databases and achieve superior performance.

Introduction : Malrotation is a common anomaly in the pediatric age group which includes a wide spectrum of anomalies of Rotation. Both acute and chronic presentations are common. Atypical malrotation not having all the features of classic mal Rotation is frequently found which a diagnostic dilemma and the management varies from centre to centre. Materials and Methods : The medical records of all patients with symptomatic malrotation, who underwent surgery between July 2001 to June 2011, were retrospectively reviewed. Patients’ presentation, management, operative findings and complications were evaluated. Results : 68 patients underwent surgery for malrotation at a median age of 2 years. Male to female ratio was 2:1. 28(41%) presented with acute symptoms and 40(59%) with chronic symptoms. 54(79%) patients had vomiting, 36 (%3%) presented with abdominal distension, 19(28%) had recurrent abdominal pain. Diagnostic laparoscopy was done in 7(10%) patients. Ladd’s band was found in 16(24%) patients and Volvulus was found at the time of surgery in 5(7%) patients. 5(7%) patients also had associated anomalies. Ladd’s procedure was done in 15(22%) patients and 23(34%) patients needed resection and anastomosis. Median length of hospital stay was 10 days. Postoperative bowel obstruction was seen in 4(6%) patients and 2(3%) patients had post operative intussusceptions. There was 2(3%) death due to septicaemia with volvulus and gangrenous gut. Conclusion : The clinical presentation and anatomy of malrotation occurs along a wide clinical and anatomic variations and a high index of suspicion is required to prevent a delay in diagnosis. JCMCTA 2011; 22(2): 17-21

A vital issue for face recognition is to represent a face image by effective and efficient features. To-date a numerous feature extraction techniques have been proposed in the literature. Among them, content based image retrieval (CBIR) using curvelet transform captures accurate texture features to represent the image. In this paper, we propose a novel face recognition method that uses curvelet texture features for face representation. Features are computed by low order statistics like mean and standard deviation of transformed face images. Since the spectral domain of curvelet has no hole or overlap, there is no loss of frequency information in face images. Moveover, such feature representation has considerably low dimension. Thus, computation within the face-space becomes easier. Furthermore, the dimension of features is independent of face image resolution. As a result, it can support face images of different resolution as input. To build the classifier, we apply PCA on the concatenated feature representation of subdivisions. We test our system with 4 and 5 levels of scales of curvelet transform. We also experiment by dividing the face image into different number of sub-divisions on three standard databases. The experimental results confirm that curvelet texture features achieve satisfactory performance for face recognition.

Face recognition is considered as a high dimensionality problem. To handle high dimensionality, a numerous methods have been proposed in literature. In this paper, we propose a novel face recognition method that efficiently solves that problem using MPEG-7 edge histogram descriptor. To the authors' knowledge, this is the first attempt to use edge histogram descriptor in face recognition. Although MPEG-7 standard represents only local edge histogram we use global and semi-global edge histogram also. We find that local edge histogram mostly helpful for face recognition. We test our system not only using the entire face image as input but also dividing the image into different sub-divisions. PCA is then applied to the edge histogram descriptors of sub-divisions in-stead of raw pixel intensity values of images which traditional methods do. Since we use normalized edge histogram, our face recognition method becomes scale, translation and rotation invariant. Furthermore, our proposed method does not necessarily require all images to be of same resolution as input. We evaluate the proposed method using ORL, Yale and Face94 face databases and achieve superior performance.

Keywords: burn; neonate; scald; flame burnDOI: http://dx.doi.org/10.3329/jcmcta.v22i1.9109 JCMCTA 2011; 22(1): 28-31