Automated Intruder Detection from Image Sequences using Minimum Volume Sets

We propose a new algorithm based on machine learning techniques for automatic intruder detection in surveillance networks.  The algorithm is theoretically founded on the concept of minimum volume sets.  Through application to image sequences from two different scenarios and comparison with some existing algorithms, we show that it is possible for our proposed algorithm to easily obtain high detection accuracy with low false alarm rates

SMART: A Subspace based Malicious Peers Detection algorithm for P2P Systems

In recent years, reputation management schemes have been proposed as promising solutions to alleviate the blindness during peer selection in distributed P2P environment where malicious peers coexist with honest ones. They indeed provide incentives for peers to contribute more resources to the system and thus promote the whole system performance. But few of them have been implemented practically since they still suffer from various security threats, such as collusion, Sybil attack and so on. Therefore, how to detect malicious peers plays a critical role in the successful work of these mechanisms, and it will also be our focus in this paper. Firstly, we define malicious peers and show their influence on the system performance. Secondly, based on Multiscale Principal Component Analysis (MSPCA) and control chart, a Subspace based MAlicious peeRs deTecting algorithm (SMART) is brought forward. SMART first reconstructs the original reputation matrix based on subspace method, and then finds malicious peers out based on Shewhart control chart. Finally, simulation results indicate that SMART can detect malicious peers efficiently and accurately

Computational Study on the Microscopic Adsorption Characteristics of Linear Alkylbenzene Sulfonates with Different Chain Lengths on Anthracite Surface

In order to explore the influence of different lengths of hydrophobic carbon chains on the diffusion characteristics of surfactants on the surface of anthracite, six linear alkyl benzene sulfonates with different hydrophobic carbon chain lengths were selected (mC, m = 8, 10, 12, 14, 16, 18; m represents the numbers of carbon atoms in the hydrophobic carbon chain), and molecular dynamics (MD) simulations were adopted. Models of surfactant-anthracite, surfactant-graphite layer, and water-surfactant-anthracite were constructed. After analyzing a series of properties such as adsorption energy, diffusion coefficient, radial distribution function (RDF), and hydrophobic tail order parameters, it was found that 12C had the highest adsorption strength on the surface of anthracite; the reason was that 12C had the highest degree of aggregation near the oxygen-containing functional groups on the surface of anthracite. Further studies had found that the hydrophobic tail chain of 12C had the strongest isotropy. The study fills the gap in the systematic study of the diffusion characteristics of linear alkylbenzene sulfonates (LAS) with different chain lengths on the surface of anthracite, enriches and develops the basic theory of coal wettability, and also provides technical ideas for the design of new surfactants and new dust suppression agents

Efficient and effective automated surveillance agents using kernel tricks

Many schemes have been presented over the years to develop automated visual surveillance systems. However, these schemes typically need custom equipment, or involve significant complexity and storage requirements. In this paper we present three software-based agents built using kernel machines to perform automated, real-time intruder detection in surveillance systems. Kernel machines provide a powerful data mining technique that may be used for pattern matching in the presence of complex data. They work by first mapping the raw input data onto a (often much) higher dimensional feature space, and then clustering in the feature space instead. The reasoning is that mapping onto the (higher-dimensional) feature space enables the comparison of additional, higher order correlations in determining patterns between the raw data points. The agents proposed here have been built using algorithms that are adaptive, portable, do not require any expensive or sophisticated components, and are lightweight and efficient having run times of the order of hundredths of a second. Through application to real image streams from a simple, run-of-the-mill closed-circuit television surveillance system, and direct quantitative performance comparison with some existing schemes, we show that it is possible to easily obtain high detection accuracy with low computational and storage complexities

Molecular dynamics simulation of the effect of SDS / SDBS on the wettability of anthracite

In order to explore the microscopic mechanism of anionic surfactants in coal mine dust removal. Using molecular dynamics simulation methods, two commonly used anionic surfactants, sodium dodecyl sulfate (SDS) and sodium dodecylbenzene sulfonate (SDBS), were selected to study their effects on the wettability of anthracite. The surface roughness and interaction energy of the surfactant anthracite adsorption system were calculated. The relative concentration distribution and radial distribution function (RDF) of the water surfactant anthracite system were analyzed. The microscopic reasons for the wettability change of anthracite were discussed.The results show that there are two ways of adsorption of anionic surfactants on anthracite, the adsorption of the head group toward the surface of the anthracite and the adsorption toward the liquid phase; this adsorption is physical adsorption, and van der Waals interaction plays a leading role in the adsorption process; The presence of benzene ring in SDBS leads to tighter adsorption on the surface of anthracite, and the adsorption configuration is more stable. The results of RDF and coordination number further show that the hydrophobicity of SDS near the ketone group of anthracite is similar to that of SDBS; the hydrophobicity of SDBS near the hydroxyl group is stronger than that of SDS, which is the main reason for the stronger hydrophobicity and greater wettability change of anthracite after adsorption by SDBS; Benzene ring plays an important role in the change of wettability of anthracite. This provides a certain basis for the selection of surfactants in coal mine dust removal. The basic theory of wettability of anthracite has been enriched and developed. The molecular dynamics simulation evaluation of the adsorption behavior and wettability changes of these two anionic surfactants is in good agreement with the existing experimental data

Unusually stronger quantum fluctuation with larger spins: Novel phenomena revealed by emergent magnetism in pressurized high-temperature superconductor FeSe

A counter-intuitive enhancement of quantum fluctuation with larger spins, together with a few novel physical phenomena, is discovered in studying the recently observed emergent magnetism in high-temperature superconductor FeSe under pressure. Starting with experimental crystalline structure from our high-pressure X-ray refinement, we analyze theoretically the stability of the magnetically ordered state with a realistic spin-fermion model. We find surprisingly that in comparison with the magnetically ordered Fe-pnictides, the larger spins in FeSe suffer even stronger long-range quantum fluctuation that diminishes their ordering at ambient pressure. This "fail-to-order" quantum spin liquid state then develops into an ordered state above 1GPa due to weakened fluctuation accompanying the reduction of anion height and carrier density. The ordering further benefits from the ferro-orbital order and shows the observed enhancement around 1GPa. We further clarify the controversial nature of magnetism and its interplay with nematicity in FeSe in the same unified picture for all Fe-based superconductors. In addition, the versatile itinerant carriers produce interesting correlated metal behavior in a large region of phase space. Our study establishes a generic exceptional paradigm of stronger quantum fluctuation with larger spins that complements the standard knowledge of insulating magnetism.Comment: 7 pages, 4 figure

Pendugaan Model Permintaan Ubi Kayu di Indonesia

Cassava (Manihot esculenta Crantz) is important commodity of Indonesia not only as forth producer after Nigeria, Thailand, and Brazil but also as source of carbohydrate. This research will use time series data among 1999-2009. The increasing of cassava production along 1971-2009 reaching 22,03 million tons. And also the projection until 2010 increase until 25,54 million tons. By this increasing, it is expected can open fissure of production and marketing in Indonesia better than before. Simultaneously test of variable contained the coming of cassava stock, another demand, cassava export, cassava consumption, and the demand of cassava last year has significant effect toward cassava demand

Peran Praktisi Dalam Pengembangan Teori Dan Proses Pembelajaran Untuk Sekolah Bisnis

The decreasing number of intakes and quality of the students, and crisis identityhave jeopardise the survival of business schools. There must be a breakthrough toovercome these hard situation. One of the solution is theories-in-use approach, whichis needed to develop the appropriate theory. The writer also suggests to recruit practitionersas the faculty members. This will encourage original theories developmentwhich is appropriate for third world countries like Indonesia. The other solutionis to send the existing lecturers to join the consulting and encourage them to havethe knowledge of practice world. By doing these, hopefully better condition will beachieved

Case fatality risk of the first pandemic wave of novel coronavirus disease 2019 (COVID-19) in China

Objective To assess the case fatality risk (CFR) of COVID-19 in mainland China, stratified by region and clinical category, and estimate key time-to-event intervals. Methods We collected individual information and aggregated data on COVID-19 cases from publicly available official sources from December 29, 2019 to April 17, 2020. We accounted for right-censoring to estimate the CFR and explored the risk factors for mortality. We fitted Weibull, gamma, and lognormal distributions to time-to-event data using maximum-likelihood estimation. Results We analyzed 82,719 laboratory-confirmed cases reported in mainland China, including 4,632 deaths, and 77,029 discharges. The estimated CFR was 5.65% (95%CI: 5.50%-5.81%) nationally, with highest estimate in Wuhan (7.71%), and lowest in provinces outside Hubei (0.86%). The fatality risk among critical patients was 3.6 times that of all patients, and 0.8-10.3 fold higher than that of mild-to-severe patients. Older age (OR 1.14 per year; 95%CI: 1.11-1.16), and being male (OR 1.83; 95%CI: 1.10-3.04) were risk factors for mortality. The time from symptom onset to first healthcare consultation, time from symptom onset to laboratory confirmation, and time from symptom onset to hospitalization were consistently longer for deceased patients than for those who recovered. Conclusions Our CFR estimates based on laboratory-confirmed cases ascertained in mainland China suggest that COVID-19 is more severe than the 2009 H1N1 influenza pandemic in hospitalized patients, particularly in Wuhan. Our study provides a comprehensive picture of the severity of the first wave of the pandemic in China. Our estimates can help inform models and the global response to COVID-19