Miracle-dz/UniFAR-FTUs huggingface dataset:
Modified Apriori Approach for Evade Intrusion Detection System

Intrusion Detection System (IDS) is a software or hardware tool that repeatedly scans and monitors events that took place in a computer or a network. A set of rules are used by Signature based Intrusion Detection Systems (IDS) to detect hostile traffic in network segments or packets, which are so important in detecting malicious and anomalous behavior over the network like known attacks that hackers look for new techniques to go unseen. Sometime, a single failure at any layer will cause the IDS to miss that attack. To overcome this problem, a technique is used that will trigger a failure in that layer. Such technique is known as Evasive technique. An Evasion can be defined as any technique that modifies a visible attack into any other form in order to stay away from being detect. The proposed system is used for detecting attacks which are going on the network and also gives actual categorization of attacks. The proposed system has advantage of getting low false alarm rate and high detection rate. So that leads into decrease in complexity and overhead on the system. The paper presents the Evasion technique for customized apriori algorithm. The paper aims to make a new functional structure to evade IDS. This framework can be used to audit IDS. This framework shows that a proof of concept showing how to evade a self-built IDS considering two publicly available datasets.
Embodiment of an Agent by Anthropomorphization of a Common Object

We propose a direct anthropomorphization to improve human-agent interaction. It agentizes an artifact by attaching humanoid parts to it. There have been many studies that can provide valuable information on using spoken directions and gestures via anthropomorphic agents such as CG (computer graphics) agents and communication robots. Our method directly anthropomorphizes the artifact through robotic bodily parts shaped like those of humans. An anthropomorphized artifact with these parts can provide information to people by giving them spoken directions and expressing themselves through body language. This makes people pay more attentions to the artifact, than when using anthropomorphic CG or robot agents. We conducted an experiment to verify the difference between receiving an explanation of the functions of the artifact using the direct anthropomorphization method and that from using the independent humanoid agent "Robovie". The results from participants' questionnaires and gazes during the experiment indicated that they noticed the target artifact and memorized the functions more quickly and easily from using the direct anthropomorphization method than from the "Robovie".
Element Segregation in Low-Metallicity Stars and the Primordial Lithium Abundance

Observational constraints on the primordial lithium abundance are important for the evaluation of the baryonic density of the universe. Its precise determination, however, suffers from uncertainties arising from the possible depletion of this element inside stars. Here we present and discuss new results for the lithium abundances in Population II stars obtained with the most recent stellar models including the best available physics. We show that it is possible to account for the general behavior of lithium observed in Population II stars without any free parameters. Macroscopic motions inside the stars are needed, but it is not necessary to specify their exact nature in order to interpret the observational data. This fact allows us to derive a parameter-free value for the primordial lithium abundance: log Lip = 2.35 ± 0.10 in the log H = 12 scale.
Multiple fundamental frequency estimation based on harmonicity and spectral smoothness

A new method for estimating the fundamental frequencies of concurrent musical sounds is described. The method is based on an iterative approach, where the fundamental frequency of the most prominent sound is estimated, the sound is subtracted from the mixture, and the process is repeated for the residual signal. For the estimation stage, an algorithm is proposed which utilizes the frequency relationships of simultaneous spectral components, without assuming ideal harmonicity. For the subtraction stage, the spectral smoothness principle is proposed as an efficient new mechanism in estimating the spectral envelopes of detected sounds. With these techniques, multiple fundamental frequency estimation can be performed quite accurately in a single time frame, without the use of long-term temporal features. The experimental data comprised recorded samples of 30 musical instruments from four different sources. Multiple fundamental frequency estimation was performed for random sound source and pitch combinations. Error rates for mixtures ranging from one to six simultaneous sounds were 1.8%, 3.9%, 6.3%, 9.9%, 14%, and 18%, respectively. In musical interval and chord identification tasks, the algorithm outperformed the average of ten trained musicians. The method works robustly in noise, and is able to handle sounds that exhibit inharmonicities. The inharmonicity factor and spectral envelope of each sound is estimated along with the fundamental frequency.
A Mass Spectrometric Analysis Method Based on PPCA and SVM for Early Detection of Ovarian Cancer

Background. Surfaced-enhanced laser desorption-ionization-time of flight mass spectrometry (SELDI-TOF-MS) technology plays an important role in the early diagnosis of ovarian cancer. However, the raw MS data is highly dimensional and redundant. Therefore, it is necessary to study rapid and accurate detection methods from the massive MS data. Methods. The clinical data set used in the experiments for early cancer detection consisted of 216 SELDI-TOF-MS samples. An MS analysis method based on probabilistic principal components analysis (PPCA) and support vector machine (SVM) was proposed and applied to the ovarian cancer early classification in the data set. Additionally, by the same data set, we also established a traditional PCA-SVM model. Finally we compared the two models in detection accuracy, specificity, and sensitivity. Results. Using independent training and testing experiments 10 times to evaluate the ovarian cancer detection models, the average prediction accuracy, sensitivity, and specificity of the PCA-SVM model were 83.34%, 82.70%, and 83.88%, respectively. In contrast, those of the PPCA-SVM model were 90.80%, 92.98%, and 88.97%, respectively. Conclusions. The PPCA-SVM model had better detection performance. And the model combined with the SELDI-TOF-MS technology had a prospect in early clinical detection and diagnosis of ovarian cancer.
Computerized Medical Imaging and Graphics Biopsy Needle Detection in Transrectal Ultrasound

Using the fusion of pre-operative MRI and real time intra-procedural transrectal ultrasound (TRUS) to guide prostate biopsy has been shown as a very promising approach to yield better clinical outcome than the routinely performed TRUS only guided biopsy. In several situations of the MRI/TRUS fusion guided biopsy, it is important to know the exact location of the deployed biopsy needle, which is imaged in the TRUS video. In this paper, we present a method to automatically detect and segment the biopsy needle in TRUS. To achieve this goal, we propose to combine information from multiple resources, including ultrasound probe stability, TRUS video background model, and the prior knowledge of needle orientation and position. The proposed algorithm was tested on TRUS video sequences which have in total more than 25,000 frames. The needle deployments were successfully detected and segmented in the sequences with high accuracy and low false-positive detection rate.
Imperfection Information, Optimal Monetary Policy and Informational Consistency

This paper examines the implications of imperfect information (II) for optimal monetary policy with a consistent set of informational assumptions for the modeller and the private sector an assumption we term the informational consistency. We use an estimated simple NK model from Levine et al. (2012), where the assumption of symmetric II significantly improves the fit of the model to US data to assess the welfare costs of II under commitment, discretion and simple Taylor-type rules. Our main results are: first, common to all information sets we find significant welfare gains from commitment only with a zero-lower bound constraint on the interest rate. Second, optimized rules take the form of a price level rule, or something very close across all information cases. Third, the combination of limited information and a lack of commitment can be particularly serious for welfare. At the same time we find that II with lags introduces a ‘tying ones hands’ effect on the policymaker that may improve welfare under discretion. Finally, the impulse response functions under our most extreme imperfect information assumption (output and inflation observed with a two-quarter delay) exhibit hump-shaped behaviour and the fiscal multiplier is significantly enhanced in this case.
Application-aware adaptive cache architecture for power-sensitive mobile processors

Today, mobile smartphones are expected to be able to run the same complex, algorithm-heavy, memory-intensive applications that were originally designed and coded for general-purpose processors. All the while, it is also expected that these mobile processors be power-conscientious as well as of minimal area impact. These devices pose unique usage demands of ultra-portability but also demand an always-on, continuous data access paradigm. As a result, this dichotomy of continuous execution versus long battery life poses a difficult challenge. This article explores a novel approach to mitigating mobile processor power consumption while abating any significant degradation in execution speed. The concept relies on efficiently leveraging both compile-time and runtime application memory behavior to intelligently target adjustments in the cache to significantly reduce overall processor power, taking into account both the dynamic and leakage power footprint of the cache subsystem. The simulation results show a significant reduction in power consumption of approximately 13% to 29%, while only incurring a nominal increase in execution time and area.
Overexpression of LRIG1 regulates PTEN via MAPK/MEK signaling pathway in esophageal squamous cell carcinoma

The present study aimed to evaluate the role of leucine-rich repeats and immunoglobulin-like domain protein 1 (LRIG1) in the regulation of phosphatase and tensin homolog (PTEN) expression in esophageal carcinogenesis. LRIG1 was overexpressed in esophageal squamous cell carcinoma (ESCC) cell lines, and the effect of LRIG1 overexpression on the mRNA and protein expression levels of PTEN was evaluated by reverse transcription-quantitative polymerase chain reaction and western blotting. Furthermore, the effects of LRIG1 overexpression on the cell cycle distribution and apoptosis of ESCC cells were examined by flow cytometry. Various cell signaling pathway inhibitors were used to assess the effects of LRIG1 on downstream signaling in ESCC cell lines. In addition, the association between LRIG1 and PTEN expression was examined in 48 samples from patients with ESCC. LRIG1 overexpression was demonstrated to downregulate PTEN expression in ESCC cell lines, and promote their proliferation and inhibit apoptosis. In addition, LRIG1-mediated suppression of PTEN expression was inhibited by the U0126 inhibitor, which suggests that LRIG1 may inhibit the activation of PTEN signaling molecules by triggering the mitogen-activated protein kinase (MAPK)/MAPK kinase 1 (MEK) signaling pathway. In conclusion, the present study demonstrated that overexpression of LRIG1 significantly and adversely affected the survival of ESCC cells, and that the MAPK/MEK signaling pathway may be responsible for the repression of PTEN expression and function.
Effect of Antenna Spacing on the Performance of Multiple Input Multiple Output LTE Downlink in an Urban Microcell

ABSTRACT The paper presents design of a 2×2 multiple input multiple output (MIMO) LTE Downlink using OFDM with 16-QAM scheme, operated in a spatial Multiplexing (SM) mode. An urban Microcell Winner channel model is assumed to investigate the performance of the system. The focus of this paper is to understand the effect of antenna spacing of end transceivers on the performance of 2×2 MIMO LTE Downlink. The performance parameters like Capacity, Throughput and Bit error rate are determined for different antenna spacing at Base station (BS) as well as at mobile station( MS) for single user. Further the quantitative superiority of closed loop MIMO over Open Loop MIMO is established and discussed. The results depicted in the paper could be of vital importance for commercial deployment of MIMO based systems to fulfill requirements of contemporary wireless baseband technology.
2016 Ieee International Conference on Big Data (big Data) Ustore: an Optimized Storage System for Enterprise Data Warehouses at Unionpay

—UnionPay's inter-bank transaction settlement platform (ITSP) generates a huge amount of bankcard transaction data everyday, recording different bankcard activities. In order to unleash the business value of these data, UnionPay has built a customized data warehouse based on Hadoop to manage and query the massive data imported from ITSP. However, the original system suffers from low storage utilization due to various types of data redundancy. Such data redundancy is caused by the long-term evolution of the system architecture. It dramatically wastes storage space, degrades query performance and leads to data inconsistency problem. In order to address these issues, we have developed UStore, an optimized storage system to reduce most data redundancies and improve query performance. In this paper, we present the design and implementation of UStore in detail. We test the performance of UStore on UnionPay's real data and the results show significant improvements in both storage utilization and query performance. To date, UStore has been deployed to process over 15 years' bankcard transaction data (over 3PB in plain text format) in UnionPay. I. INTRODUCTION As the largest bankcard association in China, Union-Pay manages tremendous (over 3PB) and increasing (over 100GB per day) structured bankcard transaction data. Each record in UnionPay dataset is a bankcard-based activity, e.g., consumption, inquiry and bank transfer. To manage and leverage such data, UnionPay employs a large scale fat table on a Hadoop based data warehouse. This table consists of over 200 distinct columns, which record rich information including trans_at (amount of the transaction), pri_acct_no (account number), settle_dt (liquidation date) and etc. For each day, thousands of analytic queries run on the distributed platform, supporting a wide range of applications for both the government and companies, such as macroeconomic analysis, customer credit rating, merchants recommendation, and customer retention. Figure 1 gives an overview of the original system in UnionPay. It employs Hadoop Distributed File System (HDFS) [1] as the distributed file system, MapReduce [1] as the compute engine, and uses Hive [2] to manage the fat table. After several times of platform migration and technology evolution, this system stores multiple tables derived from the fat table and stores many duplicate columns. The
Design of Software Model Checker

— In a few cases, modeling languages have been designed to resemble programming languages, although the focus has been on protocol designs. Some of these linguistic choices have made, and still make it feasible to more conveniently experiment with new algorithms and frameworks for analyzing system models. In this paper we will attempt to give convincing arguments for why we believe it is time for the formal methods community to shift some of its attention towards the analysis of programs written in modern programming languages. In keeping with this philosophy we have developed verification and testing environment for Java, called Java PathFinder (JPF), which integrates model checking, program analysis and testing. Part of this work has consisted of building a new Java Virtual Machine that interprets Java byte code. JPF uses state compression to handle big states, and partial order and symmetry reduction, slicing, abstraction
Encodings of Reactive Synthesis

In this talk, we present and compare several encodings of the bounded synthesis problem for linear-time temporal logic (LTL). The bounded synthesis problem for natural bound n is to decide whether there exists a strategy (generated by a transition system with n states) that satisfies an LTL specification—and in the positive case to construct such a strategy. We give an overview of previously studied encodings that use SMT, antichains, and BDDs, as well as new encodings using (quantified) propositional logics. Furthermore, we evaluate the constraint based approaches (SMT, SAT, QBF, etc.) with respect to solving time and implementation quality using certifying theory solvers.
Building Realistic Simulations for Interactive Information Retrieval

Simulation has been used within the field of Information Retrieval (IR) for many years to evaluate retrieval models and other aspects of the wider IR process. In recent years, there has been a renewed interest towards using simulation for Interactive Information Retrieval (IIR), an area which focuses on the study of human interactions with IR systems. A variety of different interaction models (e.g. click models) associated with behavioural aspects of searchers have over time been developed and evaluated using simulation in order for us to better understand the complex processes involved. Despite these advances, such models are still relatively naïve, and further work is required to make simulations of searchers more realistic. To this end, this project seeks to build more realistic simulations, using a more Complex Searcher Model (CSM). Within the CSM, each component and decision point can be varied and customised as required. The CSM can then be instantiated using components that are grounded from empirical evidence based upon actual real-world searcher behaviour and interaction data.
Optimal Output Regulation for Heterogeneous Multiagent Systems via Adaptive Dynamic Programming

In this paper, the optimal output regulation problem for partially model-free heterogeneous linear multiagent systems with disturbance generated by an exosystem is addressed by using adaptive dynamic programming and double compensator method. The topology graph for the information exchange of the agents has a spanning tree. The dynamic of individual agent is assumed to be nonidentical and of different dimensions. One distributed compensator is designed to deal with the nonidentical agents, and the other compensator is used to handle the optimal performance index. By constructing the double compensator, the distributed feedback control laws are designed to make the output of each agent synchronize with the reference output and minimize the energy of the output error simultaneously. To overcome the lack of the dynamics knowledge of each agent, a novel online policy iteration algorithm is developed to obtain the optimal feedback gain matrix. Finally, two examples are presented to illustrate the effectiveness of our results.
ESTs Analysis of Putative Genes Engaged in Polyporus umbellatus Sclerotial Development

Polyporus umbellatus is one of the most widely used and precious medicinal fungi and the underground sclerotia are known to be with great medicinal value. However, the molecular mechanisms involved in sclerotial development are poorly understood. In the present study, we constructed a forward suppression subtractive hybridization (SSH) cDNA library of Polyporus umbellatus to identify genes expressing differently between mycelium and sclerotia. In this library, a total of 1202 clones were sequenced, assembled into 222 contigs and 524 singletons which were further searched against the NCBI nonredundant (NR) protein database (E-value cutoff, 10−5). Based on sequence similarity with known proteins, 378 sequences between mycelium and sclerotial were identified and classified into different functional categories through Gene Ontology (GO), Clusters of orthologous Groups of proteins (COGs). We have finally identified a majority of differentially expressed genes (constituting 5.6% of the present library) between the two different periods. An expression level of 32 selected expressed sequence tags (ESTs) generated from the above SSH cDNA library was studied through RT-PCR. This study provides the first global overview of genes putatively involved in Polyporus umbellatus sclerotial development and provides a preliminary basis for further functional research in terms of regulated gene expression in sclerotial production.
Gender and Critical Drug Studies

This introduction to conjoined special issues of Contemporary Drug Problems and Social History of Alcohol and Drugs, the journal of the Alcohol and Drugs History Society, began with a 2015 symposium at the Baldy Center for Law and Social Policy at the University at Buffalo (SUNY), organized by co-editors Nancy D. Campbell and David Herzberg. The symposium called for incorporating gender analysis into the rapidly developing scholarship on drug use, drug trade, drug science, drug treatment, and drug policy in the United States. The special issues showcase articles that are part of a vibrant body of historical, sociological, and anthropological scholarship that explores the differential effects of drug policy, focusing on how gender—in dynamic relationship to race, class, and sexuality—is integral to virtually every aspect of drug crises including (but not limited to) the relationship between drug policy, drug treatment, and the development of mass incarceration. Gender matters at every level from the intimate and highly personalized to the broad cultural and political forces that disparately apportion vulnerability within drug commerce and the U.S. prison–industrial complex.
Surface Reconstruction based on Lower Dimensional Localized Delaunay Triangulation

We present a fast, memory efficient algorithm that generates a manifold triangular mesh S passing through a set of unorganized points P R 3. Nothing is assumed about the geometry, topology or presence of boundaries in the data set except that P is sampled from a real manifold surface. The speed of our algorithm is derived from a projection‐based approach we use to determine the incident faces on a point. We define our sampling criteria to sample the surface and guarantee a topologically correct mesh after surface reconstruction for such a sampled surface. We also present a new algorithm to find the normal at a vertex, when the surface is sampled according our given criteria. We also present results of our surface reconstruction using our algorithm on unorganized point clouds of various models.
Compressive image feature extraction by means of folding

We explore the utility of a dimensionality reducing process we term folding for the purposes of image feature extraction. We seek to discover whether image features are preserved under this process and how to efficiently extract them. The application is in size weight and power constrained imaging scenarios where an efficient implementation of this dimensionality reduction can save power and computation costs. The specific features we explore are image corners, rotation, and translation. We present algorithms for recovering these features from folded representations of images followed by simulation results showing the performance of the algorithms when operating on real images.
SHOCK WAVES IN ONE-DIMENSIONAL HEISENBERG FERROMAGNETS

We use SU(2) coherent state path integral formulation with the stationary phase approximation to investigate, both analytically and numerically, the existence of shock waves in the one- dimensional Heisenberg ferromagnets with anisotropic exchange interaction. As a result we show the existence of shock waves of two types,"bright" and "dark", which can be interpreted as moving magnetic domains.