Dictionary based action video classification with action bank

Classifying action videos became challenging problem in computer vision community. In this work, action videos are represented by dictionaries which are learned by online dictionary learning (ODL). Here, we have used two simple measures to classify action videos, reconstruction error and projection. Sparse approximation algorithm LASSO is used to reconstruct test video and reconstruction error is calculated for each of the dictionaries. To get another discriminative measure projection, the test vector is projected onto the atoms in the dictionary. Minimum reconstruction error and maximum projection give information regarding the action category of the test vector. With action bank as a feature vector, our best performance is 59.3% on UCF50 (benchmark is 57.9%), 97.7% on KTH (benchmark is 98.2%)and 23.63% on HMDB51 (benchmark is 26.9%)

A Super-Fast Distributed Algorithm for Bipartite Metric Facility Location

The \textit{facility location} problem consists of a set of \textit{facilities} $\mathcal{F}$, a set of \textit{clients} $\mathcal{C}$, an \textit{opening cost} $f_i$ associated with each facility $x_i$, and a \textit{connection cost} $D(x_i,y_j)$ between each facility $x_i$ and client $y_j$. The goal is to find a subset of facilities to \textit{open}, and to connect each client to an open facility, so as to minimize the total facility opening costs plus connection costs. This paper presents the first expected-sub-logarithmic-round distributed O(1)-approximation algorithm in the $\mathcal{CONGEST}$ model for the \textit{metric} facility location problem on the complete bipartite network with parts $\mathcal{F}$ and $\mathcal{C}$. Our algorithm has an expected running time of $O((\log \log n)^3)$ rounds, where $n = |\mathcal{F}| + |\mathcal{C}|$. This result can be viewed as a continuation of our recent work (ICALP 2012) in which we presented the first sub-logarithmic-round distributed O(1)-approximation algorithm for metric facility location on a \textit{clique} network. The bipartite setting presents several new challenges not present in the problem on a clique network. We present two new techniques to overcome these challenges. (i) In order to deal with the problem of not being able to choose appropriate probabilities (due to lack of adequate knowledge), we design an algorithm that performs a random walk over a probability space and analyze the progress our algorithm makes as the random walk proceeds. (ii) In order to deal with a problem of quickly disseminating a collection of messages, possibly containing many duplicates, over the bipartite network, we design a probabilistic hashing scheme that delivers all of the messages in expected-$O(\log \log n)$ rounds.Comment: 22 pages. This is the full version of a paper that appeared in DISC 201

Intrusion Detection and Anomaly Detection System Using Sequential Pattern Mining

Nowadays the security methods from password protected access up to firewalls which are used to secure the data as well as the networks from attackers. Several times these type of security methods are not enough to protect data. We can consider the use of Intrusion Detection Systems (IDS) is the one way to secure the data on critical systems. Most of the research work is going on the effectiveness and exactness of the intrusion detection, but these attempts are for the detection of the intrusions at the operating system and network level only. It is unable to detect the unexpected behavior of systems due to Malicious transactions in databases. The method used for spotting any interferes on the information in the form of database known as database intrusion detection. It relies on enlisting the execution of a transaction. After that, if the recognized pattern is aside from those regular patterns actual is considered as an intrusion. But the identified problem with this process is that the accuracy algorithm which is used may not identify entire patterns. This type of challenges can affect in two ways. 1) Missing of the database with regular patterns. 2) The detection process neglects some new patterns. Therefore we proposed sequential data mining method by using new Modified Apriori Algorithm. The algorithm upturns the accurateness and rate of pattern detection by the process. The Apriori algorithm with modifications is used in the proposed model

Stress Intensity Factors for Part-Through Surface Cracks in Hollow Cylinders

Flaws resulting from improper welding and forging are usually modeled as cracks in flat plates, hollow cylinders or spheres. The stress intensity factor solutions for these crack cases are of great practical interest. This report describes some recent efforts at improving the stress intensity factor solutions for cracks in such geometries with emphasis on hollow cylinders. Specifically, two crack configurations for cylinders are documented. One is that of a surface crack in an axial plane and the other is a part-through thumb-nail crack in a circumferential plane. The case of a part-through surface crack in flat plates is used as a limiting case for very thin cylinders. A combination of the two cases for cylinders is used to derive a relation for the case of a surface crack in a sphere. Solutions were sought which cover the entire range of the geometrical parameters such as cylinder thickness, crack aspect ratio and crack depth. Both the internal and external position of the cracks are considered for cylinders and spheres. The finite element method was employed to obtain the basic solutions. Power-law form of loading was applied in the case of flat plates and axial cracks in cylinders and uniform tension and bending loads were applied in the case of circumferential (thumb-nail) cracks in cylinders. In the case of axial cracks, the results for tensile and bending loads were used as reference solutions in a weight function scheme so that the stress intensity factors could be computed for arbitrary stress gradients in the thickness direction. For circumferential cracks, since the crack front is not straight, the above technique could not be used. Hence for this case, only the tension and bending solutions are available at this time. The stress intensity factors from the finite element method were tabulated so that results for various geometric parameters such as crack depth-to-thickness ratio (a/t), crack aspect ratio (a/c) and internal radius-to-thickness ratio (R/t) or the crack length-to-width ratio (2c/W) could be obtained by interpolation and extrapolation. Such complete tables were then incorporated into the NASA/FLAGRO computer program which is widely used by the aerospace community for fracture mechanics analysis

Overview of hepatocellular carcinoma: from molecular aspects to future therapeutic options.

Hepatocellular carcinoma (HCC) is the seventh most highly prevalent malignant tumor globally and the second most common cause of mortality. HCC develops with complex pathways that occur through multistage biological processes. Non-alcoholic fatty liver disease, metabolic-associated fatty liver disease, alcoholic liver disease, autoimmune hepatitis, hepatitis B, and hepatitis C are the causative etiologies of HCC. HCC develops as a result of epigenetic changes, protein-coding gene mutations, and altered signaling pathways. Biomarkers and potential therapeutic targets for HCC open up new possibilities for treating the disease. Immune checkpoint inhibitors are included in the treatment options in combination with molecular targeted therapy

Incremental Medians via Online Bidding

In the k-median problem we are given sets of facilities and customers, and distances between them. For a given set F of facilities, the cost of serving a customer u is the minimum distance between u and a facility in F. The goal is to find a set F of k facilities that minimizes the sum, over all customers, of their service costs. Following Mettu and Plaxton, we study the incremental medians problem, where k is not known in advance, and the algorithm produces a nested sequence of facility sets where the kth set has size k. The algorithm is c-cost-competitive if the cost of each set is at most c times the cost of the optimum set of size k. We give improved incremental algorithms for the metric version: an 8-cost-competitive deterministic algorithm, a 2e ~ 5.44-cost-competitive randomized algorithm, a (24+epsilon)-cost-competitive, poly-time deterministic algorithm, and a (6e+epsilon ~ .31)-cost-competitive, poly-time randomized algorithm. The algorithm is s-size-competitive if the cost of the kth set is at most the minimum cost of any set of size k, and has size at most s k. The optimal size-competitive ratios for this problem are 4 (deterministic) and e (randomized). We present the first poly-time O(log m)-size-approximation algorithm for the offline problem and first poly-time O(log m)-size-competitive algorithm for the incremental problem. Our proofs reduce incremental medians to the following online bidding problem: faced with an unknown threshold T, an algorithm submits "bids" until it submits a bid that is at least the threshold. It pays the sum of all its bids. We prove that folklore algorithms for online bidding are optimally competitive.Comment: conference version appeared in LATIN 2006 as "Oblivious Medians via Online Bidding

A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography

IMPORTANCE: The identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network-based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans. OBJECTIVE: To develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study included participants with iAMD at baseline and who either progressed or did not progress to GA within the subsequent 13 months. Participants were included from centers in 4 US states. Data set 1 included patients from the Age-Related Eye Disease Study 2 AREDS2 (Ancillary Spectral-Domain Optical Coherence Tomography) A2A study (July 2008 to August 2015). Data sets 2 and 3 included patients with imaging taken in routine clinical care at a tertiary referral center and associated satellites between January 2013 and January 2023. The stored imaging data were retrieved for the purpose of this study from July 1, 2022, to February 1, 2023. Data were analyzed from May 2021 to July 2023. EXPOSURE: A position-aware convolutional neural network with proactive pseudointervention was trained and cross-validated on Bioptigen SD-OCT volumes (data set 1) and validated on 2 external data sets comprising Heidelberg Spectralis SD-OCT scans (data sets 2 and 3). MAIN OUTCOMES AND MEASURES: Prediction of progression to GA within 13 months was evaluated with area under the receiver-operator characteristic curves (AUROC) as well as area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. RESULTS: The study included a total of 417 patients: 316 in data set 1 (mean [SD] age, 74 [8]; 185 [59%] female), 53 in data set 2, (mean [SD] age, 83 [8]; 32 [60%] female), and 48 in data set 3 (mean [SD] age, 81 [8]; 32 [67%] female). The AUROC for prediction of progression from iAMD to GA within 1 year was 0.94 (95% CI, 0.92-0.95; AUPRC, 0.90 [95% CI, 0.85-0.95]; sensitivity, 0.88 [95% CI, 0.84-0.92]; specificity, 0.90 [95% CI, 0.87-0.92]) for data set 1. The addition of expert-annotated SD-OCT features to the model resulted in no improvement compared to the fully autonomous model (AUROC, 0.95; 95% CI, 0.92-0.95; P = .19). On an independent validation data set (data set 2), the model predicted progression to GA with an AUROC of 0.94 (95% CI, 0.91-0.96; AUPRC, 0.92 [0.89-0.94]; sensitivity, 0.91 [95% CI, 0.74-0.98]; specificity, 0.80 [95% CI, 0.63-0.91]). At a high-specificity operating point, simulated clinical trial recruitment was enriched for patients progressing to GA within 1 year by 8.3- to 20.7-fold (data sets 2 and 3). CONCLUSIONS AND RELEVANCE: The fully automated, position-aware deep-learning algorithm assessed in this study successfully predicted progression from iAMD to GA over a clinically meaningful time frame. The ability to predict imminent GA progression could facilitate clinical trials aimed at preventing the condition and could guide clinical decision-making regarding screening frequency or treatment initiation