SPI: Automated Identification of Security Patches via Commits

Security patches in open source software, providing security fixes to identified vulnerabilities, are crucial in protecting against cyber attacks. Security advisories and announcements are often publicly released to inform the users about potential security vulnerability. Despite the National Vulnerability Database (NVD) publishes identified vulnerabilities, a vast majority of vulnerabilities and their corresponding security patches remain beyond public exposure, e.g., in the open source libraries that are heavily relied on by developers. As many of these patches exist in open sourced projects, the problem of curating and gathering security patches can be difficult due to their hidden nature. An extensive and complete security patches dataset could help end-users such as security companies, e.g., building a security knowledge base, or researcher, e.g., aiding in vulnerability research. To efficiently curate security patches including undisclosed patches at large scale and low cost, we propose a deep neural-network-based approach built upon commits of open source repositories. First, we design and build security patch datasets that include 38,291 security-related commits and 1,045 Common Vulnerabilities and Exposures (CVE) patches from four large-scale C programming language libraries. We manually verify each commit, among the 38,291 security-related commits, to determine if they are security related. We devise and implement a deep learning-based security patch identification system that consists of two composite neural networks: one commit-message neural network that utilizes pretrained word representations learned from our commits dataset and one code-revision neural network that takes code before revision and after revision and learns the distinction on the statement level. Our system leverages the power of the two networks for Security Patch Identification. Evaluation results show that our system significantly outperforms SVM and K-fold stacking algorithms. The result on the combined dataset achieves as high as 87.93% F1-score and precision of 86.24%. We deployed our pipeline and learned model in an industrial production environment to evaluate the generalization ability of our approach. The industrial dataset consists of 298,917 commits from 410 new libraries that range from a wide functionalities. Our experiment results and observation on the industrial dataset proved that our approach can identify security patches effectively among open sourced projects.

Livelihood functions of fisher households under a cold-water production environment of Kashmir, Jammu and Kashmir, India

This study comprehensively investigated the livelihood security scenario of fisher households (FHs) employing the CARE framework with little modifications, in Kashmir, India. Primary data for this study was collected from selected FHs, and a regression function was fitted to quantify the determinants of livelihood security. The findings revealed that fishing has been their dominant livelihood option. The landholding owned by the households was meagre enough to carry out farming or domesticate animals on commercial lines. Poor capital endowments place them at less livelihood security level; however, the respondents with diversified income have a relatively higher index value for livelihood. The regression estimates indicated that barring social and natural capital, all forms of capital have a significant role to play in securing their livelihood. Poor livelihood security, coupled with less income flow, has made their survival vulnerable to various distresses and health disorders, including the prevalence of Infant & Maternal Mortality. Their dietary intake was undesirably less than their dietary recommendations. The COVID-19 pandemic was perceived as a shock to their livelihood security. Further, public investment, which is pertinent for the growth of the fisheries sector, has shown a discouraging trend. The study concluded with a few policy suggestions for securing the livelihood of the fisher community.

The J. Derek Bewley Career Lecture. Seeds–plants–crops–biodiversity–environment–people: illustrating understanding and ideas

The J. Derek Bewley Career Lectures presented at the triennial meetings of the International Society of Seed Science support early-career seed scientists by providing retrospective views, from those late in their careers, of lessons learned and future implications. Ambition, ability, inspiration, foresight, hard work and opportunity are obvious career requirements. The importance of mentoring and teamwork combined with the clear communication of results, understanding and ideas are emphasized. The role of illustration in research, and its dissemination, is outlined: illustration can support hypothesis development, testing and communication. Climate change may perturb the production of high-quality seed affecting conservation as well as agriculture, horticulture and forestry. An illustrative synthesis of the current understanding of temporal aspects of the effects of seed production environment on seed quality (assessed by subsequent seed storage longevity) is provided for wheat (Triticum aestivum L.) and rice (Oryza sativa L.). Seed science research can contribute to complex global challenges such as future food supplies from seed-propagated crops in our changing climate whilst conserving biological diversity (through seed ecology and technologies such as ex situ plant genetic resources conservation by long-term seed storage in genebanks), but only if that research can be – and then is – applied.

A Novel Embedding Model Based on a Transition System for Building Industry-Collaborative Digital Twin

Recently, the production environment has been rapidly changing, and accordingly, correct mid term and short term decision-making for production is considered more important. Reliable indicators are required for correct decision-making, and the manufacturing cycle time plays an important role in manufacturing. A method using digital twin technology is being studied to implement accurate prediction, and an approach utilizing process discovery was recently proposed. This paper proposes a digital twin discovery framework using process transition technology. The generated digital twin will unearth its characteristics in the event log. The proposed method was applied to actual manufacturing data, and the experimental results demonstrate that the proposed method is effective at discovering digital twins.

Multi-Seam Tracking With a Portable Robotic Welding System in Unstructured Environments

Most welding manufacturing of the heavy industry, such as shipbuilding and construction, is carried out in an unstructured workspace. The term Unstructured indicates the production environment is irregular, changeable and without model. In this case, the changeable workpiece position, workpiece shape, environmental background, and environmental illumination should be carefully considered. Because of such complicated characteristics, the welding is currently being relied on the manual operation, resulting in high cost, low efficiency and quality. This work proposes a portable robotic welding system and a novel seam tracking method. Compared to existing methods, it can cope with more complex general spatial curve weld. Firstly, the tracking pose of the robot is modeled by a proposed dual-sequence tracking strategy. On this basis, the working parameters can be adjusted to avoid robot-workpiece collision around the workpiece corners during the tracking process. By associating the forward direction of the welding torch with the viewpoint direction of the camera, it solves the problem that the weld feature points are prone to be lost in the tracking process by conventional methods. Point cloud registration is adopted to globally locate the multi-segment welds in the workpiece, since the system deployment location is not fixed. Various experiments on single or multiple welds under different environmental conditions show that even if the robot is deployed in different positions, it can reach the starting point of the weld smoothly and accurately track along the welds.

Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric

In order to manufacture products at low cost, machine learning (ML) is increasingly used in production, especially in high wage countries. Therefore, we introduce our PREFERML AutoML system, which is adapted to the production environment. The system is designed to predict production errors and to help identifying the root cause. It is particularly important to produce results for further investigations that can also be used by quality engineers. Quality engineers are not data science experts and are usually overwhelmed with the settings of an algorithm. Because of this, our system takes over this task and delivers a fully optimized ML model as a result. In this paper, we give a brief overview of what results can be achieved with a state-of-the-art classifier. Moreover, we present the results with optimized tree-based algorithms based on RandomSearchCV and HyperOpt hyperparameter tuning. The algorithms are optimized based on multiple metrics, which we will introduce in the following sections. Based on a cost-oriented metric we can show an improvement for companies to predict the outcome of later product tests. Further, we compare the results from the mentioned optimization approaches and evaluate the needed time for them.

Prevalence and antimicrobial susceptibility of Listeria spp. isolated from bulk raw milk in north-eastern Poland

Purpose of research: The aim of the study was to determine the presence of Listeria spp. strains in the milk samples obtained from dairy farms in north-eastern Poland and to determine the profile of resistance to antibiotics recommended in the treatment of listeriosis. Material and methods: 500 samples of bulk milk were analyzed. Milk samples were obtained from dairy farms located in Warmia and Mazury region in Poland. Chronic mastitis, requiring frequent and long-term use of antibiotics has been documented in these herds. Isolation of Listeria spp. was performed according to the standard procedure PN-EN ISO 11290-1: 2017-07. Antibiotic resistance testing was performed by the disc diffusion method according to the Clinical & Laboratory Standards Institute (CLSI) recommendations. Results: In total, out of 500 samples of pooled milk, based on biochemical properties, 8 isolates were confirmed as belonging to the genus Listeria (1.6%). The further identification of Listeria strains on the basis of MicrobactListeria12L showed that 3 strains (3/8, 37.5%) belonged to L. monocytogenes species and 5 strains (5/8, 62.5%) belonged to L. innocua species. The analysis of sensitivity to commonly used antimicrobial agents showed that all isolates, both belonging to L. monocytogenes and L. innocua species, were sensitive to ampicillin. Multidrug resistance, defined as resistance to at least three classes of antibiotics, was confirmed among four isolates (50%). Research summary: The studies undertaken revealed that raw milk can pose a risk for public health due to the prevalence of pathogenic Listeria spp. among which multidrug resistant strains are present. It is therefore necessary to rationalize the use of antibiotics and to monitor bacterial resistance in the food production environment.

Growing season climate affects phenological development, seed yield and seed quality of dill (Anethum graveolens)

Dill seed production was investigated over two seasons, comprising a spring growing cycle (SGC) and an autumn growing cycle (AGC). The effects of growing cycle on phenological traits, yield and quality of dill seeds formed on different umbels of the mother plant were investigated. Significant differences were noted in the flowering period, seed yield and quality parameters. The SGC resulted in a shorter time from sowing to bolting and flowering initiation compared with the AGC. Plant height, number of umbels/plant, number of umbelets/umbel, umbel diameter, umbel length and seed weight/plant increased in the AGC. In contrast, decreased germination at 20/30°C and at 13°C, and increased mean germination time at 20/30°C in AGC indicated lower seed quality compared with SGC. Primary umbels produced the best yield and higher quality seeds, followed by the secondary umbels while tertiary umbels gave poor yield and quality seeds in both SGC and AGC. In conclusion, AGC was advisable for higher seed yield, but SGC resulted in higher seed quality compared with the AGC. This highlights the need to select a suitable growing cycle to guarantee high seed yield and quality for each seed production environment.

Different Sweet Orange–Rootstock Combinations Infected by Candidatus Liberibacter asiaticus under Greenhouse Conditions: Effects on the Scion

The devastating citrus disease huanglongbing (HLB) associated with the phloem-limited bacteria Candidatus Liberibacter asiaticus (CLas) has caused a more than 70% reduction in citrus production since its discovery in Florida in 2005. Most citrus scion cultivars are sensitive to HLB, whereas some cultivars used as rootstocks are tolerant. Using such tolerant rootstocks can help trees to cope better with the disease’s impact. Evaluating rootstock effects on a grafted scion in the field takes many years, but shorter-term evaluation is imperative to aid in rootstock selection for an HLB-endemic production environment. In this study, we investigated grafted healthy and CLas-infected citrus trees under controlled greenhouse conditions. The objectives were to identify traits suitable for assessing grafted tree tolerance in advance of longer-term field studies and aiding in the selection of superior rootstock cultivars. We assessed 10 commercially important rootstocks grafted with ‘Valencia’ sweet orange scion and with known field performance. At 6, 9, 15, and 21 months after graft inoculation (mai), leaf CLas titers were determined and canopy health was evaluated. Plants were destructively sampled at 21 mai to assess plant biomasses and other physiological and horticultural variables. There was little influence of the rootstock cultivar on CLas titers. Surprisingly, few HLB foliar disease symptoms and no differences in soluble and nonsoluble carbohydrate concentrations were measured in infected compared with healthy plants, despite high CLas titers and significant reductions in plant biomasses. Most trees on rootstocks with trifoliate orange parentage were less damaged by HLB than other rootstocks, although results did not always agree with reported field performance. Among the different variables measured, leaf size appeared to be most predictive for grafted tree assessment of HLB sensitivity. The results of this study provide a better understanding of the strengths and weaknesses of assessing rootstock influence on grafted tree performance in a controlled greenhouse environment. Although such studies provide valuable information for cultivar tolerance to HLB, other rootstock traits will ultimately contribute to field survival and productivity in an HLB endemic production environment.

Different Sweet Orange‒Rootstock Combinations Infected by Candidatus Liberibacter asiaticus under Greenhouse Conditions: Effects on the Roots

Grafting a scion onto a rootstock results in physical and physiological changes in plant growth and development, which can affect tree vigor, productivity, and tolerance to stress and disease. Huanglongbing (HLB) is one of the most destructive citrus diseases and has become endemic in Florida since its introduction in 2005. It is associated with the phloem-limited bacteria Candidatus Liberibacter asiaticus (CLas), which cause severe metabolic disruptions in affected plants. Although most scion cultivars are highly susceptible, some rootstock cultivars are tolerant and allow the grafted tree to cope better with the disease. The objectives of this study were to identify rootstock traits that can be used to assess cultivars under controlled greenhouse conditions in advance of longer-term field trials. We used 10 commercially important rootstocks with different genetic backgrounds and known field performance in graft combination with ‘Valencia’ sweet orange scion. Trees were graft-inoculated with CLas and compared against mock-inoculated trees. Tree health and CLas populations were assessed regularly, and root growth was monitored using a minirhizotron imaging system. Plants were excavated and destructively sampled 21 months after inoculation to assess biomass distributions and other CLas-induced effects. We found significant differences between healthy and infected trees for most variables measured, regardless of the rootstock. In contrast to leaf CLas titers, root titers were significantly influenced by the rootstock, and highest levels were measured for ‘Ridge’ sweet orange and sour orange. Root growth and root biomasses were reduced upon infection but differences among rootstocks did not always agree with reported field performances. Despite severe biomass reductions plants maintained their relative distribution of biomass among different components of the root system, and no dead roots were observed. Root respiration was reduced by CLas infection and was overall higher in tolerant cultivars suggesting its potential as a physiological marker. This study improves our knowledge about the strengths and weaknesses of assessing rootstock traits of grafted trees in a controlled greenhouse setting. Results from the study suggest that in addition to HLB tolerance, other rootstock traits will ultimately have major contributions to field survival and productivity of the grafted trees in an HLB endemic production environment.