Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding

Superpixel segmentation has become a crucial pre-processing tool to reduce computation in many computer vision applications. In this paper, a superpixel extraction algorithm based on a seed strategy of contour encoding (SSCE) for infrared images is presented, which can generate superpixels with high boundary adherence and compactness. Specifically, SSCE can solve the problem of superpixels being unable to self-adapt to the image content. First, a contour encoding map is obtained by ray scanning the binary edge map, which ensures that each connected domain belongs to the same homogeneous region. Second, according to the seed sampling strategy, each seed point can be extracted from the contour encoding map. The initial seed set, which is adaptively scattered based on the local structure, is capable of improving the capability of boundary adherence, especially for small regions. Finally, the initial superpixels limited by the image contour are generated by clustering and refined by merging similar adjacent superpixels in the region adjacency graph (RAG) to reduce redundant superpixels. Experimental results on a self-built infrared dataset and the public datasets BSD500 and 3Dircadb demonstrate the generalization ability in grayscale and medical images, and the superiority of the proposed method over several state-of-the-art methods in terms of accuracy and compactness.

The Effect of Invigoration Using Polyethylene Glycol And Ultra Fine Bubble on Improving of Sengon Seeds (Falcataria Moluccana Miq.) Quality After Two Years Storage

Utilization of seeds storage results in decreased plant productivity. The increase can be used by treating seeds before planting through the technique of invigoration. This purpose of the study is to assess the effectiveness of invigoration methods using polyethylene glycol (PEG 6000) and ultrafine bubbles (UFB) to improve viability and vigor of sengon seeds (Falcataria moluccana). Seed agieng using 96 % ethanol was carried out to obtain the diversity of seedlot viability as the materials for testing the effectiveness of invigoration treatments. A complete random design was used to test the effectiveness of 5 invigoration treatments, i.e., seed without invigoration treatment, soaking in PEG 6000 -0.8 Mpa, soaking in PEG 6000 -1.2 Mpa, soaking in UFB water injected by environmental air, and soaking in UFB water injected by oxygen 99 %, with soaking time is 24 hours for each treatment. Seed agieng resulted three classes of seed viability, i.e. 62 % (initial seed), 83 % (seed agieng for 30 minutes) and 57 % (seed agieng for 60 minutes). In the condition of seed germination before treatment (DBA) 57 % and DBA 62%, invigoration treatments were significantly affected on seed germination capacity, but not significantly different in DBA 83 %. The soaking treatment of UFB injected by oxigen 99 % was able to improve the germination parameters (germination capacity, germination rate, and vigor index) on the sengon seeds with DBA 57 %. For seeds with DBA 62 %, the soaking treatment in UFB injected by environmental air was provided the best germination capacity, T50, and vigor index. The treatment of UFB injected by oxygen 99 % was more effective to improve the seed with very low viability and vigor (DBA 57 %). In general, improving of seed viability and vigor is more effective by using UFB than PEG 6000.

Chapter 14. Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning

Deep learning has proven effective for various application tasks, but its applicability is limited by the reliance on annotated examples. Self-supervised learning has emerged as a promising direction to alleviate the supervision bottleneck, but existing work focuses on leveraging co-occurrences in unlabeled data for task-agnostic representation learning, as exemplified by masked language model pretraining. In this chapter, we explore task-specific self-supervision, which leverages domain knowledge to automatically annotate noisy training examples for end applications, either by introducing labeling functions for annotating individual instances, or by imposing constraints over interdependent label decisions. We first present deep probabilistic logic (DPL), which offers a unifying framework for task-specific self-supervision by composing probabilistic logic with deep learning. DPL represents unknown labels as latent variables and incorporates diverse self-supervision using probabilistic logic to train a deep neural network end-to-end using variational EM. Next, we present self-supervised self-supervision (S4), which adds to DPL the capability to learn new self-supervision automatically. Starting from an initial seed self-supervision, S4 iteratively uses the deep neural network to propose new self supervision. These are either added directly (a form of structured self-training) or verified by a human expert (as in feature-based active learning). Experiments on real-world applications such as biomedical machine reading and various text classification tasks show that task-specific self-supervision can effectively leverage domain expertise and often match the accuracy of supervised methods with a tiny fraction of human effort.

Design of a Cryptographically Secure Pseudo Random Number Generator with Grammatical Evolution

This work investigates the potential of evolving an initial seed with Grammatical Evolution (GE), for the construction of cryptographically secure (CS) pseudo-random number generator (PRNG). We harness the flexibility of GE as an entropy source for returning initial seeds. The initial seeds returned by GE demonstrate an average entropy value of 7.920261600000001 which is extremely close to the ideal value of 8. The initial seed combined with our proposed approach, control_flow_incrementor, is used to construct both, GE-PRNG and GE-CSPRNG.The random numbers generated with CSPRNG meet the prescribed National Institute of Standards and Technology (NIST) SP800-22 requirements. Monte Carlo simulations established the efficacy of the PRNG. The experimental setup was designed to estimate the value for pi, in which 100,000,000 random numbers were generated by our system and which resulted in returning the value of pi to 3.146564000, with a precision up to six decimal digits. The random numbers by GE-PRNG were compared against those generated by Python’s rand() function for sampling. The sampling results, when measured for accuracy against twenty-nine real world regression datasets, showed that GE-PRNG had less error when compared to Python’s rand() against the ground truths in seventeen of those, while there was no discernible difference in the remaining twelve.

Framework design for competitive marketing mechanism in Iraqi social media

Several organizations in Iraq manufacture similar commodities in this aggressive social trading. The objective of these organizations is diffusing information about their commodities publicly for popularity of the commodities in social media. More returns result in popular commodities and vice versa. The development of a framework incorporating two organizations engaging to broaden the information to the large media has been undertaken. The organizations first identified their initial seed points concurrently and then data was scattered as per the Independent Cascade Model (ICM). The major objective of the organizations is the identification of seed points for the diffusion of data to several points in social media. Significant is also how fast data diffusion can be done. Data effect will arise from either none, one or more nodes in a social interconnection. Evaluation is also accomplished on the number of fraction parts in various sections are affected by the different rates of data diffusion. The simulation result for suggested framework presented better outcomes result for random network 1 and random network 2 comparing with regular network. This framework is used a Hotellingframwork of competition.

Characterisation of rice fallow period for increasing cropping intensity in Khordha district of Odisha

The possibility of increasing the cropping intensity in Khordha district was assessed by block-wise characterisation of rainfall, soil texture, available water holding capacity of soil and district level temperature during the rice fallow period as well as deciding the appropriate sowing window for both kharif and rabi crops. More than 50% chances of occurrence of wet weeks at the beginning of the kharif season (20-22 SMW) indicated that the summer ploughing and initial seed bed preparation could be taken up during the period followed by sowing of rice from 23 SMW. The 42-43 SMW with greater than 30% initial probability of wet week at 20 mm threshold limit. Considering the length of growing period (LGP) available, the adjustment of rice variety duration by 10-15 days in medium land and advancement of pulse sowing almost by 15 days before harvest of the rice crop (Pira crop) in low land was registered as the best su suitable option for rabi pulses to ensure better utilisation of rice fallow. In addition to these, harvesting surplus water during kharif and storing in farm ponds for judicious utilisation during rabi season further enhanced the possibility of utilisation of rice fallow and increasing the cropping intensity. In the event of delayed monsoon the rice duration has to be adjusted accordingly not to sacrifice the designated suitable period for rabi pulses.

Wheat yield and seed physiological quality as influenced by seed vigor, nitrogen fertilization and edaphoclimatic conditions

Wheat is one of the most important cereals grown in the world, and in Brazil, increasing national production is still a challenge. Nitrogen (N) supply can favor grain yield and the physiological quality of wheat seeds. However, the definition of adequate N rate and fertilization timing must consider genotype, cultivation environment, and initial seed vigor level. The aim of this work was to evaluate the effect of initial seed vigor and the combinations of rates and timings of N application on grain yield and seed physiological quality of wheat cultivars under different edaphoclimatic conditions. The experiment was carried out in Londrina and Ponta Grossa, state of Paraná, in a randomized block design in a 2 × 2 × 7 factorial scheme, with four replicates. Treatments consisted of two seed vigor levels (vigorous and non-vigorous seeds), two wheat cultivars (BRS Gralha-Azul and BRS Sabiá) and seven combinations of fertilization timings and N rates (kg ha-1) (control-0N; 20N at sowing and 60N at tillering; 40N at sowing; 80N at sowing; 40N at sowing and 40N at tillering; 40N at tillering; 80N at tillering). Number of emerged seedlings, vegetation index, shoot dry matter, number of fertile spikes m-2 and grain yield were evaluated. Additionally, the physiological potential of seeds produced in Londrina was evaluated by the testes of first count, germination, seedling emergence in sand and emergence speed index. The climatic conditions during the experiment, in both cultivation environments, were similar to average historical records, with some periods of water deficit. The highest grain yield was obtained with the BRS Gralha-Azul in Londrina, and with BRS Sabiá in Ponta Grossa. The use of vigorous seeds favored the stand establishment and the response of plants to N fertilization. The treatments 40N + 40N, and 40N + 0N favored the majority of evaluated variables. Both cultivars showed potential for the production of high physiological potential seeds in Londrina environment. Inadequate N supply impairs wheat grain yield and seed production.

Heterogeneous Influence Maximization Through Community Detection in Social Networks

The problem of identifying a seed set composed of K nodes that increase influence spread over a social network is known as influence maximization (IM). Past works showed this problem to be NP-hard and an optimal solution to this problem using greedy algorithms achieved only 63% of spread. However, this approach is expensive and suffered from performance issues like high computational cost. Furthermore, in a network with communities, IM spread is not always certain. In this paper, heterogeneous influence maximization through community detection (HIMCD) algorithm is proposed. This approach addresses initial seed nodes selection in communities using various centrality measures, and these seed nodes act as sources for influence spread. A parallel influence maximization is applied with the aid of seed node set contained in each group. In this approach, graph is partitioned and IM computations are done in a distributed manner. Extensive experiments with two real-world datasets reveals that HCDIM achieves substantial performance improvement over state-of-the-art techniques.

Seed Biologists Beware: End-of-Trial Estimation of Initial Viability May Be Error-Prone Due to Unexpected Seed Death during the Experiment

Seed viability is routinely measured on seeds that fail to germinate at the end of an experiment. Together with the number of germinants, this is used to estimate viability of the seeds at start of the experiment (i.e., initial viability) and provides the comparative basis on which germination success is determined. We used this standard procedure on 40 Leucadendron species subjected to oscillating temperatures, heat and/or smoke pre-treatments to examine the extent to which they raised germination levels above that of the untreated controls. 16 species showed significantly different levels of estimated initial seed viability between treatments when they should have been unaffected. Loss of viability during the trial was an order of magnitude greater than annual loss during cold storage, which was usually negligible. Lowest levels of estimated initial viability occurred among the poorly germinating controls and confirmed that the heat and smoke treatments had little effect on viability. Species with soil-stored seeds were more vulnerable to this artefact than those with plant-stored seeds. We caution against the routine use of end-of-trial germination and viability of ungerminated seeds as an estimate of initial viability in determining germination success of various treatments. The preference is for estimates of initial viability to be undertaken on a separate sample of seeds in association with the trial.

Neutronic Evaluation of MSBR System Using MCNP Code

The concept of Molten Salt Reactor use Th to breed fissile 233U, where an initial source of fissile material needs to be provided. However, there is no available 233U and so; the fissile fuel supply is one of the unresolved problems. Thus, it is necessary to use existing fissile materials such as 235U or Pu to produce 233U. Current studies analyze the fuel transition from 235U/Th or Pu/Th to 233U/Th and, in this context, the present work evaluates the criticality and the neutron flux of MSBR (Molten Salt Breeder Reactor) considering the fuel: (i) mix of Th and enriched U; (ii) the combination of Th and reprocessed Pu; and (iii) matrix of reprocessed Pu/minor actinides (MAs) and Th. The goal is to verify which of these fuels can be used as initial fissile supply. The MSBR core was simulated by MCNPX 2.6.0 code and the criticality model presents similar behavior of previous studies. The results show that reprocessed fuels could have a potential to be used as initial fissile supply, but these fuels present a neutron flux profile less flattens than traditional 233U/Th. It is possible that a new distribution of fuel elements may improve this profile and future simulations will be performed to evaluate this behavior. The uranium, must has high enrichment value to be used as initial seed. Other studies need be performed to evaluates the uranium enrichment and the U/Th ratio that produces similar core criticality to traditional fuel.