111 research outputs found
Study of Security Issues in Pervasive Environment of Next Generation Internet of Things
Internet of Things is a novel concept that semantically implies a world-wide
network of uniquely addressable interconnected smart objects. It is aimed at
establishing any paradigm in computing. This environment is one where the
boundary between virtual and physical world is eliminated. As the network gets
loaded with hitherto unknown applications, security threats also become
rampant. Current security solutions fail as new threats appear to de-struct the
reliability of information. The network has to be transformed to IPv6 enabled
network to address huge number of smart objects. Thus new addressing schemes
come up with new attacks. Real time analysis of information from the
heterogeneous smart objects needs use of cloud services. This can fall prey to
cloud specific security threats. Therefore need arises for a review of security
threats for a new area having huge demand. Here a study of security issues in
this domain is briefly presented.Comment: 12 pages, CISIM 201
Sleep Deprivation Attack Detection in Wireless Sensor Network
Deployment of sensor network in hostile environment makes it mainly
vulnerable to battery drainage attacks because it is impossible to recharge or
replace the battery power of sensor nodes. Among different types of security
threats, low power sensor nodes are immensely affected by the attacks which
cause random drainage of the energy level of sensors, leading to death of the
nodes. The most dangerous type of attack in this category is sleep deprivation,
where target of the intruder is to maximize the power consumption of sensor
nodes, so that their lifetime is minimized. Most of the existing works on sleep
deprivation attack detection involve a lot of overhead, leading to poor
throughput. The need of the day is to design a model for detecting intrusions
accurately in an energy efficient manner. This paper proposes a hierarchical
framework based on distributed collaborative mechanism for detecting sleep
deprivation torture in wireless sensor network efficiently. Proposed model uses
anomaly detection technique in two steps to reduce the probability of false
intrusion.Comment: 7 pages,4 figures, IJCA Journal February 201
Typing pattern analysis for fake profile detection in social Media
Nowadays, interaction with fake profiles of a genuine user in social media is a common problem. General users may not easily identify profiles created by fake users. Although various research works are going on all over the world to detect fake profiles in social media, focus of this paper is to remove additional efforts in detection procedure. Behavioral biometrics like typing pattern of users can be considered to classify genuine profile and fake profile without disrupting normal activities of the users. In this paper, DEEP_ID model is designed to detect fake profiles in Facebook like social media considering typing patterns like keystroke, mouse-click, and touch stroke. Proposed model can silently detect the profiles created by fake users when they type or click in social media from desktop, laptop, or touch devices. DEEP_ID model can also identify whether genuine profiles have been hacked by fake users or not in the middle of the session. The objective of proposed work is to demonstrate the hypothesis that user recognition algorithms applied to raw data can perform better if requirement for feature extraction can be avoided, which in turn can remove the problem of inappropriate attribute selection. Proposed DEEP_ID model is based on multi-view deep neural network, where network layers can learn data representation for user recognition based on raw data of typing pattern without feature selection and extraction. Proposed DEEP_ID model has achieved better results compared to traditional machine learning classifiers. It provides strong evidence that the stated hypothesis is valid. Evaluation results indicate that Deep_ID model is highly accurate in profile detection and efficient enough to perform fast detection
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Modeling Conventionalization and Predictability within MWEs at the Brain Level
While expressions have traditionally been binarized as compositional and noncompositional in linguistic theory, Multiword Expressions (MWEs) demonstrate finer-grained distinctions. Using Association Measures like Pointwise Mutual Information and Dice\u27s Coefficient, MWEs can be characterized as having different degrees of conventionalization and predictability. Our goal is to investigate how these gradiences could reflect cognitive processes. In this study, fMRI recordings of naturalistic narrative comprehension is used to probe to what extent these computational measures and the cognitive processes they could operationalize are observable during on-line sentence processing. Our results show that Dice\u27s Coefficent, representing lexical predictability, is a better predictor of neural activation for processing MWEs. Overall our experimental approach demonstrates how we can test the cognitive plausibility of computational metrics by comparing it against neuroimaging data
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Variability in the reported management of pulmonary metastases in osteosarcoma.
Nearly 20% of patients with newly diagnosed osteosarcoma have detectable metastases at diagnosis; the majority of which occur in the lungs. There are no established recommendations for the timing and modality of metastasectomy. Members of the Connective Tissue Oncology Society (CTOS) were emailed an anonymous 10-min survey assessing their management practices for pulmonary findings at the time of an osteosarcoma diagnosis. The questionnaire presented three scenarios and discussed the choice to perform surgery, the timing of resection, and the choice of surgical procedure. Analyses were stratified by medical profession. One hundred and eighty-three physicians responded to our questionnaire. Respondents were comprised of orthopedic surgeons (37%), medical oncologists (31%), pediatric oncologists (22%), and other medical subspecialties (10%). There was variability among the respondents in the management of the pulmonary nodules. The majority of physicians chose to resect the pulmonary nodules following neoadjuvant chemotherapy (46-63%). Thoracotomy was the preferred technique for surgical resection. When only unilateral findings were present, the majority of physicians did not explore the contralateral lung. The majority of respondents did not recommend resection if the pulmonary nodule disappeared following chemotherapy. The survey demonstrated heterogeneity in the management of pulmonary metastases in osteosarcoma. Prospective trials need to evaluate whether these differences in management have implications for outcomes for patients with metastatic osteosarcoma
Neural Circuit Architectural Priors for Embodied Control
Artificial neural networks for motor control usually adopt generic
architectures like fully connected MLPs. While general, these tabula rasa
architectures rely on large amounts of experience to learn, are not easily
transferable to new bodies, and have internal dynamics that are difficult to
interpret. In nature, animals are born with highly structured connectivity in
their nervous systems shaped by evolution; this innate circuitry acts
synergistically with learning mechanisms to provide inductive biases that
enable most animals to function well soon after birth and learn efficiently.
Convolutional networks inspired by visual circuitry have encoded useful biases
for vision. However, it is unknown the extent to which ANN architectures
inspired by neural circuitry can yield useful biases for other AI domains. In
this work, we ask what advantages biologically inspired ANN architecture can
provide in the domain of motor control. Specifically, we translate C. elegans
locomotion circuits into an ANN model controlling a simulated Swimmer agent. On
a locomotion task, our architecture achieves good initial performance and
asymptotic performance comparable with MLPs, while dramatically improving data
efficiency and requiring orders of magnitude fewer parameters. Our architecture
is interpretable and transfers to new body designs. An ablation analysis shows
that constrained excitation/inhibition is crucial for learning, while weight
initialization contributes to good initial performance. Our work demonstrates
several advantages of biologically inspired ANN architecture and encourages
future work in more complex embodied control.Comment: NeurIPS 202
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