IP Geolocation through Reverse DNS

IP Geolocation databases are widely used in online services to map end-user IP addresses to their geographical location. However, they use proprietary geolocation methods, and in some cases they have poor accuracy. We propose a systematic approach to use reverse DNS hostnames for geolocating IP addresses, with a focus on end-user IP addresses as opposed to router IPs. Our method is designed to be combined with other geolocation data sources. We cast the task as a machine learning problem where, for a given hostname, we first generate a list of potential location candidates, and then we classify each hostname and candidate pair using a binary classifier to determine which location candidates are plausible. Finally, we rank the remaining candidates by confidence (class probability) and break ties by population count. We evaluate our approach against three state-of-the-art academic baselines and two state-of-the-art commercial IP geolocation databases. We show that our work significantly outperforms the academic baselines and is complementary and competitive with commercial databases. To aid reproducibility, we open source our entire approach and make it available to the academic community.

Evaluating Trends of Land Productivity Change and Their Causes in the Han River Basin, China: In Support of SDG Indicator 15.3.1

The Han River Basin is a main agricultural production area and a water source for the middle route of the South-to-North Water Diversion Project in China. Over the past 20 years, human exploitation and ecological construction have disturbed the sustainability of land productivity in the Han River Basin. Theil–Sen trend analysis, Mann–Kendall statistical test, and Hurst index methods were applied to examine spatial–temporal trends and sustainability characteristics of land net primary productivity (NPP) change in the Han River Basin from 2001 to 2019 using MOD17A3 NPP product, natural, and socio-economic data obtained from Google Earth Engine (GEE). The findings demonstrated that the interannual variation of land NPP exhibited a fluctuating upward trend, with a more pronounced growth rate from 2001 to 2010 than from 2011 to 2019. The spatial heterogeneity of land NPP was evident, with high values in the west and low values in the east. Of the basin area, 57.82% presented a significant increase in land NPP, while only 0.96% showed a significant decrease. In the future, land NPP in the Han River Basin will present sustained growth. The results were also compared with Trends.Earth’s calculations for the SDG 15.3.1 sub-indicator of land productivity. In addition, the spatial heterogeneity of factors influencing land NPP change was explored using a multiscale geographically weighted regression (MGWR) model. Precipitation and population count were the dominant factors in most regions. Besides, precipitation, population count, and human modification all exhibited inhibitory effects on the increase in land NPP except for elevation. The research can provide a scientific basis for tracking land degradation neutrality (LDN) progress and achieving sustainable socio-ecological development of the Han River Basin.

Vaccine effectiveness of Pfizer-BioNTech and Oxford-AstraZeneca to prevent severe COVID-19 in Costa Rica by September and October 2021: A nationwide, observational study of hospitalisations prevalence.

Objective To estimate the dose-dependent effectiveness of coronavirus disease (COVID-19) vaccines to prevent severe illness in real-world conditions of Costa Rica, after the Delta variant became dominant. Design Observational study; secondary analysis of hospitalisation prevalence. Setting Nationwide adult population, Costa Rica. Participants All 3.67 million adults residents in Costa Rica by mid-2021. Public aggregated data of 5978 hospital records from 14th September to 20th October, 2021 and 6.1 million vaccination doses administered. Interventions Vaccination with Pfizer-BioNTech (78%) and Oxford-AstraZeneca (22%). Main outcome measures Prevalence of COVID-19-related hospitalisations Results Vaccine effectiveness to prevent hospitalisation (VEH) was estimated as 93.4% (95% confidence interval [CI]: 93.0 to 93.9) for complete vaccination and 76.7% (CI: 75.0 to 78.3) for single-dose vaccination among adults of all ages. VEH was lower and more uncertain among older adults aged 58 years and above: 92% (CI: 91% to 93%) for those who had received full vaccination and 64% (CI: 58% to 69%) for those who had received partial vaccination. Single-dose VEH declined over time during the study period, especially in the older age group. Estimates were sensitive to possible errors in the population count used to determine the residual number of unvaccinated people in groups with high vaccine coverage. Conclusion The Costa Rican vaccination programme that administered Pfizer and Oxford vaccines are highly effective to prevent COVID-19-related hospitalisations after the Delta variant had become dominant. Moreover, a single dose is reasonably effective, justifying the continuation of the national policy of postponing the application for the second dose of the Pfizer vaccine to accelerate the vaccination and increase the number of people being vaccinated. Timely monitoring of vaccine effectiveness is important to detect eventual failures and motivate the public based on information that the vaccinations are effective.

Automatic Building Detection with Polygonizing and Attribute Extraction from High-Resolution Images

Buildings can be introduced as a fundamental element for forming a city. Therefore, up-to-date building maps have become vital for many applications, including urban mapping and urban expansion analysis. With the development of deep learning, segmenting building footprints from high-resolution remote sensing imagery has become a subject of intense study. Here, a modified version of the U-Net architecture with a combination of pre- and post-processing techniques was developed to extract building footprints from high-resolution aerial imagery and unmanned aerial vehicle (UAV) imagery. Data pre-processing with the logarithmic correction image enhancing algorithm showed the most significant improvement in the building detection accuracy for aerial images; meanwhile, the CLAHE algorithm improved the most concerning UAV images. This study developed a post-processing technique using polygonizing and polygon smoothing called the Douglas–Peucker algorithm, which made the building output directly ready to use for different applications. The attribute information, land use data, and population count data were applied using two open datasets. In addition, the building area and perimeter of each building were calculated as geometric attributes.

The Influence of Vaccination of Broiler Chickens and Turkeys with Live E. coli Attenuated Vaccine on E. coli Population Properties and TRT Vaccination Efficacy

Colibacillosis is one of the major causes of economic losses in the poultry industry. Vaccination against E. coli is attracting increasing interest. The aim of the study was to evaluate the influence of vaccination with live, aroA gene-deleted vaccine on the structure and properties of field E. coli population and its potential impact on TRT vaccination efficacy in broiler chickens and turkeys. We performed three independent experiments on farms: (1) with antibiotic-free broiler chickens, (2) with conventional broiler chickens and (3) with broiler turkeys. In experiment 1, we have recorded an approx. 0–15% prevalence of multi-susceptible E. coli strains in the first production cycle. Starting from production cycle number two, after vaccination introduction, successive significant increases in E. coli susceptibility emerged, reaching 100% of strains at the end of production cycle 3. Increased E. coli susceptibility remained for three production cycles after vaccination withdrawal. In experiments 2 (2 production cycles) and 3 (1 production cycle), we recorded similar tendencies of E. coli susceptibility profile change. In experiments 1 and 2, the E. coli population count was lower after vaccination. In experiments 2 and 3, no negative influence of E. coli vaccination on the level of specific antibodies against TRT was recorded.

The Use of Probiotics in the Reduction of Campylobacter spp. Prevalence in Poultry

Campylobacter spp. are widely distributed microorganisms, many of which are commensals of gastrointestinal tract in multiple animal species, including poultry. Most commonly detected are C. jejuni and C. coli. Although infections are usually asymptomatic in poultry, poultry meat and products represent main sources of infection with these bacteria to humans. According to recent EFSA report, campylobacteriosis is the most commonly reported zoonotic disease. In 2018, EFSA Panel on Biological Hazards indicated that use of feed and water additives is the second most likely strategy that can be successful in minimizing Campylobacter spp. colonization rate in broiler chickens. One of those feed and water additives are probiotics. From numerous research papers it can be concluded that probiotics exhibit plenty of mechanisms of anti-Campylobacter activity, which were evaluated under in vitro conditions. These results, to some extent, can explain the efficacy of probiotics in in vivo studies, although different outcome can be observed under these two laboratory conditions. Probiotics are capable of reducing Campylobacter spp. population count in poultry gastrointestinal tract and they can reduce carcass contamination. Potential modes of anti-Campylobacter activity of probiotics, results of in vivo studies and studies performed at a farm level are widely discussed in the paper.

Accelerating Population Count with a Hardware Co-Processor for MicroBlaze

This paper proposes a Field-Programmable Gate Array (FPGA)-based hardware accelerator for assisting the embedded MicroBlaze soft-core processor in calculating population count. The population count is frequently required to be executed in cyber-physical systems and can be applied to large data sets, such as in the case of molecular similarity search in cheminformatics, or assisting with computations performed by binarized neural networks. The MicroBlaze instruction set architecture (ISA) does not support this operation natively, so the count has to be realized as either a sequence of native instructions (in software) or in parallel in a dedicated hardware accelerator. Different hardware accelerator architectures are analyzed and compared to one another and to implementing the population count operation in MicroBlaze. The achieved experimental results with large vector lengths (up to 217) demonstrate that the best hardware accelerator with DMA (Direct Memory Access) is ~31 times faster than the best software version running on MicroBlaze. The proposed architectures are scalable and can easily be adjusted to both smaller and bigger input vector lengths. The entire system was implemented and tested on a Nexys-4 prototyping board containing a low-cost/low-power Artix-7 FPGA.

Quantitative and Qualitative Impacts of Selected Arthropod Venoms on the Larval Haemogram of the Greater Wax Moth, Galleria mellonella (Linnaeus) (Lepidoptera: Pyralidae)

The greater wax moth, Galleria mellonella (Linnaeus) (Lepidoptera: Pyralidae) is the most destructive pest of honey bee, Apis mellifera Linnaeus (Hymenoptera: Apidae), throughout the world. The present study was conducted to determine the quantitative and qualitative impairing effects of the arthropod venoms, viz., death stalker scorpion Leiurus quinquestriatus (Hemprich & Ehrenberg) venom (SV), oriental Hornet (wasp) Vespa orientalis Linnaeus venom (WV) and Apitoxin of A. mellifera (AP) on the larval haemogram. For this purpose, the 3rd instar larvae were treated with LC50 of each of these venoms (3428.9, 2412.6, and 956.16 ppm, respectively). The haematological investigation was conducted in haemolymph of the 5th and 7th (last) instar larvae. The important results could be summarized as follows. Five basic types of the freely circulating haemocytes in the haemolymph of last instar (7th) larvae of G. mellonella had been identified: Prohemocytes (PRs), Plasmatocytes (PLs), Granulocytes (GRs), Spherulocytes (SPs) and Oenocytoids (OEs). All venoms unexceptionally prohibited the larvae to produce normal hemocyte population (count). No certain trend of disturbance in the differential hemocyte counts of circulating hemocytes in larvae of G. mellonella after treatment with the arthropod venoms. Increasing or decreasing population of the circulating hemocytes seemed to depend on the potency of the venom, hemocyte type and the larval instar. In PRs of last instar larvae, some cytopathological features had been observed after treatment with AP or WV, but SV failed to cause cytopathological features. With regard to PLs, some cytopathological features had been observed after treatment with AP while both SV and WV failed to cause cytopathological features in this hemocyte type. No venom exhibited cytopathological effects on GRs, SPs or OEs.

Binary Precision Neural Network Manycore Accelerator

This article presents a low-power, programmable, domain-specific manycore accelerator, Binarized neural Network Manycore Accelerator (BiNMAC), which adopts and efficiently executes binary precision weight/activation neural network models. Such networks have compact models in which weights are constrained to only 1 bit and can be packed several in one memory entry that minimizes memory footprint to its finest. Packing weights also facilitates executing single instruction, multiple data with simple circuitry that allows maximizing performance and efficiency. The proposed BiNMAC has light-weight cores that support domain-specific instructions, and a router-based memory access architecture that helps with efficient implementation of layers in binary precision weight/activation neural networks of proper size. With only 3.73% and 1.98% area and average power overhead, respectively, novel instructions such as Combined Population-Count-XNOR , Patch-Select , and Bit-based Accumulation are added to the instruction set architecture of the BiNMAC, each of which replaces execution cycles of frequently used functions with 1 clock cycle that otherwise would have taken 54, 4, and 3 clock cycles, respectively. Additionally, customized logic is added to every core to transpose 16×16-bit blocks of memory on a bit-level basis, that expedites reshaping intermediate data to be well-aligned for bitwise operations. A 64-cluster architecture of the BiNMAC is fully placed and routed in 65-nm TSMC CMOS technology, where a single cluster occupies an area of 0.53 mm 2 with an average power of 232 mW at 1-GHz clock frequency and 1.1 V. The 64-cluster architecture takes 36.5 mm 2 area and, if fully exploited, consumes a total power of 16.4 W and can perform 1,360 Giga Operations Per Second (GOPS) while providing full programmability. To demonstrate its scalability, four binarized case studies including ResNet-20 and LeNet-5 for high-performance image classification, as well as a ConvNet and a multilayer perceptron for low-power physiological applications were implemented on BiNMAC. The implementation results indicate that the population-count instruction alone can expedite the performance by approximately 5×. When other new instructions are added to a RISC machine with existing population-count instruction, the performance is increased by 58% on average. To compare the performance of the BiNMAC with other commercial-off-the-shelf platforms, the case studies with their double-precision floating-point models are also implemented on the NVIDIA Jetson TX2 SoC (CPU+GPU). The results indicate that, within a margin of ∼2.1%--9.5% accuracy loss, BiNMAC on average outperforms the TX2 GPU by approximately 1.9× (or 7.5× with fabrication technology scaled) in energy consumption for image classification applications. On low power settings and within a margin of ∼3.7%--5.5% accuracy loss compared to ARM Cortex-A57 CPU implementation, BiNMAC is roughly ∼9.7×--17.2× (or 38.8×--68.8× with fabrication technology scaled) more energy efficient for physiological applications while meeting the application deadline.

ETHICAL COMPETENCY DAN STRATEGIC COMPETENCY TERHADAP PERTUMBUHAN BISNIS MAHASISWA

In order to become an economic stimulator, or an entrepreneur, ability, knowledge, and skill are required. The purpose of this research is to understand the influence of entrepreneurial competencies such as ethical competency and strategic competency to the growth of business created by the university students. The population count for this research is 204 students, with 136 samples of International Business Management students from batch 2016, who are in Startup guild, and those whose business has been going on for more than 2 semesters. The data was collected using questionnaires distributed online as well as offline. The analysis tool used for this research was SPSS with regression analysis method. The result for this research are ethical competency does not influent business growth, while on the other hand, strategic competency influents business growth. Keywords: Ethical Competency, Strategic Competency, Business Growth