Survey on Encroachment Sensing Scheme over the MANET

MANET (Mobile ad hoc network) is a collection of mobile nodes which dynamically self-organizes in erratic and transitory network topologies. Nodes in MANET can move autonomously in any direction and continuously changing the topology over the period. Each single node works evenly as a source and a recipient. MANET are more inclined towards security issues due to open medium and wide distribution of mobile nodes. It is vital to construct effective intrusion detection processes to preserve MANET from attacks. This paper introduces the various IDS schemes over MANETs, their pros and cons. This paper will be valuable to classify the suitable IDS scheme for a particular attack. DOI: 10.17762/ijritcc2321-8169.16048

Comparative evaluation of dynamic hip screw and proximal femoral nail for fracture of intertrochanteric femur

Background: The aim of the present study was to compare the result in terms of rate of union, time of ambulation and functional recovery of fracture intertrochanteric femur treated by dynamic hip screw (DHS) and proximal femoral interlocking nail (PFN) and to compare complications in terms of implant failure, infection, blood loss and C arm exposure in both groups.Methods: This was a prospective study of 92 cases, 38 cases were treated by PFN and 54 cases were treated by DHS. Patients were followed up at 6, 12, 18 and 24 weeks. The results were compared for functional outcome using Palmer and Parker score and also for various complications.Results: Comparison of mobility score at six month follow up period revealed  the PFN group to be significantly more mobile (5.8 Vs. 4.19 respectively, p <0.001) than the DHS  group. In our study 6 patients managed with DHS (6.52%) developed superficial wound infection which responded to intravenous antibiotics. No patient with PFN had wound infection. Only 2 patients in the PFN group and 12 patients in the DHS group had persistent pain at the incision site.Conclusions: Dynamic hip screw fixation of these fracture requires less preoperative time,  is associated with less exposure to radiation but the blood loss is much higher. On the contrary PFN allows faster mobilization and greater mobility scores at six months

Consent recommender system: A case study on LinkedIn settings

Privacy is an increasing concern in the digital world, especially when it has become a common knowledge that even high profile enterprises process data without data-subject’s consent. In certain cases where data-subject’s consent was taken, it was not linked to the proper purpose of processing. To address this growing concern, newer privacy regulations and laws are emerging to empower a data-subject with informed and explicit consent through which she can allow or revoke usage of her personal data. However, it has been shown that privacy self-management does not provide the expected results. This is mainly due to information overload as data-subjects use multiple services entailing variety of purposes, and hence, resulting in a very large number of consent requests. This may lead to consent fatigue as data-subject is now expected to provide informed consent for each associated purpose. The consent fatigue in data-subjects can lead to either incorrect decision making or opting for default values provided by the enterprise, and thus, defeating the purpose of new data privacy regulations. In this work, we discuss the factors influencing the informed consent of a data-subject. Further, we propose a ‘consent recommender system’ based on Factorization Machines (FMs) to assist the data-subject and thereby avoiding consent fatigue. Our consent recommender system effectively models the interaction between the different factors which influence a data-subject’s informed consent. We discuss how this setup extends for cold start data-subjects facing the decision problem with consent requests from multiple enterprises. Additionally, we demonstrate the scenario of consent recommendation as a prediction problem with minimum attributes available from LinkedIn’s privacy settings

Gradient-based data subversion attack against binary classifiers

Machine learning based data-driven technologies have shown impressive performances in a variety of application domains. Most enterprises use data from multiple sources to provide quality applications. The reliability of the external data sources raises concerns for the security of the machine learning techniques adopted. An attacker can tamper the training or test datasets to subvert the predictions of models generated by these techniques. Data poisoning is one such attack wherein the attacker tries to degrade the performance of a classifier by manipulating the training data. In this work, we focus on label contamination attack in which an attacker poisons the labels of data to compromise the functionality of the system. We develop Gradient-based Data Subversion strategies to achieve model degradation under the assumption that the attacker has limited-knowledge of the victim model. We exploit the gradients of a differentiable convex loss function (residual errors) with respect to the predicted label as a warm-start and formulate different strategies to find a set of data instances to contaminate. Further, we analyze the transferability of attacks and the susceptibility of binary classifiers. Our experiments show that the proposed approach outperforms the baselines and is computationally efficient

Influence Based Defense Against Data Poisoning Attacks in Online Learning

Data poisoning is a type of adversarial attack on training data where an attacker manipulates a fraction of data to degrade the performance of machine learning model. There are several known defensive mechanisms for handling offline attacks, however defensive measures for online learning, where data points arrive sequentially, have not garnered similar interest. In this work, we propose a defense mechanism to minimize the degradation caused by the poisoned training data on a learner's model in an online setup. Our proposed method utilizes an influence function which is a classic technique in robust statistics. Further, we supplement it with the existing data sanitization methods for filtering out some of the poisoned data points. We study the effectiveness of our defense mechanism on multiple datasets and across multiple attack strategies against an online learner

On (θ,Θ)(\theta, \Theta)-cyclic codes and their applications in constructing QECCs

Let $\mathbb F_q$ be a finite field, where $q$ is an odd prime power. Let $R=\mathbb{F}_q+u\mathbb{F}_q+v\mathbb{F}_q+uv\mathbb F_q$ with $u^2=u,v^2=v,uv=vu$. In this paper, we study the algebraic structure of $(\theta, \Theta)$-cyclic codes of block length $(r,s )$ over $\mathbb{F}_qR.$ Specifically, we analyze the structure of these codes as left $R[x:\Theta]$-submodules of $\mathfrak{R}_{r,s} = \frac{\mathbb{F}_q[x:\theta]}{\langle x^r-1\rangle} \times \frac{R[x:\Theta]}{\langle x^s-1\rangle}$. Our investigation involves determining generator polynomials and minimal generating sets for this family of codes. Further, we discuss the algebraic structure of separable codes. A relationship between the generator polynomials of $(\theta, \Theta)$-cyclic codes over $\mathbb F_qR$ and their duals is established. Moreover, we calculate the generator polynomials of dual of $(\theta, \Theta)$-cyclic codes. As an application of our study, we provide a construction of quantum error-correcting codes (QECCs) from $(\theta, \Theta)$-cyclic codes of block length $(r,s)$ over $\mathbb{F}_qR$. We support our theoretical results with illustrative examples.Comment: 30 pages, 4 table

An Emerging Trend in Tablet Technology:- Floating Tablets of Ranitidine HCl

The rationale of this research was to prepare a gastroretentive drug delivery system of Ranitidine HCL. Floating Drug delivery system used to target drug release in the stomach or to the upper part of the intestine. The oral delivery of Ranitidine is tested by preparing a non-disintegrating floating dosage form, which increase its absorption in the stomach by increasing the drug’s gastric residence time. The polymer PVC and Sodium bicarbonate was used as the gas–generating agents. Sodium bicarbonate causes the tablets to floats for more then 24hr. The prepared tablets were evaluated on their physicochemical properties and drug release characters. In-vitro release studies indicate that the Ranitidine release form the floating dosage form was uniform followed zero order release. A combination of sodium bicarbonate (70mg) and citric acid (15mg) was found to achieve Optimum in vitro buoyancy. The tablets with methocel K100 were found to float for longer duration of time as compared to formulations containing methocel K15M. The drug release from the tablets was sufficiently sustained.Keywords: Ranitidine; Floating tablets; Methoce