Efficient learning of large sets of locally optimal classification rules

Conventional rule learning algorithms aim at finding a set of simple rules, where each rule covers as many examples as possible. In this paper, we argue that the rules found in this way may not be the optimal explanations for each of the examples they cover. Instead, we propose an efficient algorithm that aims at finding the best rule covering each training example in a greedy optimization consisting of one specialization and one generalization loop. These locally optimal rules are collected and then filtered for a final rule set, which is much larger than the sets learned by conventional rule learning algorithms. A new example is classified by selecting the best among the rules that cover this example. In our experiments on small to very large datasets, the approach's average classification accuracy is higher than that of state-of-the-art rule learning algorithms. Moreover, the algorithm is highly efficient and can inherently be processed in parallel without affecting the learned rule set and so the classification accuracy. We thus believe that it closes an important gap for large-scale classification rule induction.Comment: article, 40 pages, Machine Learning journal (2023

An Outbreak of Severe Infections with Community-Acquired MRSA Carrying the Panton-Valentine Leukocidin Following Vaccination

Background: Infections with community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) are emerging worldwide. We investigated an outbreak of severe CA-MRSA infections in children following out-patient vaccination. Methods and Findings: We carried out a field investigation after adverse events following immunization (AEFI) were reported. We reviewed the clinical data from all cases. S. aureus recovered from skin infections and from nasal and throat swabs were analyzed by pulse-field gel electrophoresis, multi locus sequence typing, PCR and microarray. In May 2006, nine children presented with AEFI, ranging from fatal toxic shock syndrome, necrotizing soft tissue infection, purulent abscesses, to fever with rash. All had received a vaccination injection in different health centres in one District of Ho Chi Minh City. Eight children had been vaccinated by the same health care worker (HCW). Deficiencies in vaccine quality, storage practices, or preparation and delivery were not found. Infection control practices were insufficient. CA-MRSA was cultured in four children and from nasal and throat swabs from the HCW. Strains from children and HCW were indistinguishable. All carried the Panton-Valentine leukocidine (PVL), the staphylococcal enterotoxin B gene, the gene complex for staphylococcal-cassette-chromosome mec type V, and were sequence type 59. Strain HCM3A is epidemiologically unrelated to a strain of ST59 prevalent in the USA, althoughthey belong to the same lineage. Conclusions. We describe an outbreak of infections with CA-MRSA in children, transmitted by an asymptomatic colonized HCW during immunization injection. Consistent adherence to injection practice guidelines is needed to prevent CA-MRSA transmission in both in- and outpatient settings

Awareness and preparedness of healthcare workers against the first wave of the COVID-19 pandemic: A cross-sectional survey across 57 countries.

BACKGROUND: Since the COVID-19 pandemic began, there have been concerns related to the preparedness of healthcare workers (HCWs). This study aimed to describe the level of awareness and preparedness of hospital HCWs at the time of the first wave. METHODS: This multinational, multicenter, cross-sectional survey was conducted among hospital HCWs from February to May 2020. We used a hierarchical logistic regression multivariate analysis to adjust the influence of variables based on awareness and preparedness. We then used association rule mining to identify relationships between HCW confidence in handling suspected COVID-19 patients and prior COVID-19 case-management training. RESULTS: We surveyed 24,653 HCWs from 371 hospitals across 57 countries and received 17,302 responses from 70.2% HCWs overall. The median COVID-19 preparedness score was 11.0 (interquartile range [IQR] = 6.0-14.0) and the median awareness score was 29.6 (IQR = 26.6-32.6). HCWs at COVID-19 designated facilities with previous outbreak experience, or HCWs who were trained for dealing with the SARS-CoV-2 outbreak, had significantly higher levels of preparedness and awareness (p<0.001). Association rule mining suggests that nurses and doctors who had a 'great-extent-of-confidence' in handling suspected COVID-19 patients had participated in COVID-19 training courses. Male participants (mean difference = 0.34; 95% CI = 0.22, 0.46; p<0.001) and nurses (mean difference = 0.67; 95% CI = 0.53, 0.81; p<0.001) had higher preparedness scores compared to women participants and doctors. INTERPRETATION: There was an unsurprising high level of awareness and preparedness among HCWs who participated in COVID-19 training courses. However, disparity existed along the lines of gender and type of HCW. It is unknown whether the difference in COVID-19 preparedness that we detected early in the pandemic may have translated into disproportionate SARS-CoV-2 burden of disease by gender or HCW type

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

Frequent itemsets mining for big data

Frequent Itemsets Mining (FIM) is a fundamental mining model and plays an important role in Data Mining. It has a vast range of application fields and can be employed as a key calculation phase in many other mining models such as Association Rules, Correlations, Classifications, etc. Generally speaking, FIM counts the frequencies of co-occurrence items, called itemsets, in records of distinct items from a transaction-oriented dataset. The mining model discovers all frequent itemsets whose frequencies are not lesser than a given threshold, called support threshold (). There have been many serial algorithms with different approaches to address the execution performance and consumed memory. However, in the current era of Big Data, the serial algorithms are inadequate to face the problems of running time and memory scalability for very large-scale datasets. Big Data has come out with such challenges for FIM, but it leaves big motivations for us. In order to confront these challenges, we have proposed solutions with three methodological approaches: incremental mining, shared-memory parallel, and distributed parallel. First of all, we propose the IPPC tree and IFIN algorithm which provides incremental tree construction for PPC tree and incremental mining for one of the state-of-the-art algorithms FIN. IPPC tree construction is independent to support threshold () and the order of items. Therefore, the tree allows a previously constructed tree to be built up with a new additional dataset without wasting time to rebuild the tree with the old dataset. IFIN also possesses its own incremental mining in which some portions of the mining task are skipped when mining with difference values. In the scenario of incremental data accumulation, and especially the mining time at different values with an unchanged dataset, our experimental results showed that IFIN was the most efficient in time and memory consuming compared with the well-known algorithm FP-Growth, and two state-of-the-art ones, FIN and PrePost+. Secondly, we propose a shared-memory parallel solution for our incremental algorithm IFIN, named IFIN+. In the solution, most portions of the serial version were redesigned to increase the efficiency and computational independence for convenience in parallel computation with the load balance model, Work-Pool. As a result, IFIN+s computational throughput and efficiency increase significantly compared with those of its serial version. ^Thirdly, we have realized that in the case of datasets comprising a large number of distinguishing items but just a small percentage of frequent items, IPPC tree becomes to lose its advantages of running time and memory for the tree construction compared to those of FIN and PrePost+. Therefore, an improved version of the IPPC tree was proposed, called IPPC+, to increase the tree construction performance. The main idea is that child nodes are placed in a certain order, for example, item name-based order, which the binary-search can be applied to accelerate finding child nodes merged with items in transactions. Fourthly, we apply our second and third solutions for the state-of-the-art algorithm PrePost+ to run as the locally powerful algorithm in our distributed parallel algorithm, named DP3 (Distributed PrePostPlus), which operates in Master-Slaves model. Slaves mine and send local frequent itemsets and support counts to the Master for aggregations. In the case of tremendous numbers of itemsets transferred between the Slaves and Master, the computational load at the Master, therefore, is extremely heavy if there is not the support from our complete FPO tree (Frequent Patterns Organization) which provides optimal compactness for light data transfers and highly efficient aggregations with pruning ability. Processing phases of the Slaves and Master are designed for memory scalability and shared-memory parallel in Work-Pool model so as to utilize the computational power of multi-core CPUs. Besides, load balance in different aspects is also considered thoroughly for the best performance. We conducted experiments on both synthetic and real datasets, and the empirical results have shown that our algorithm far outperforms the well-known distributed algorithm PFP and other three recently high-performance ones Dist-Eclat, BigFIM, and MapFIM. ^Lastly, with the same purpose like our FPO tree, we propose a bijective mapping (bijection) which maps numeric sets with fixed or variant sizes to numbers (mapping numbers) and can convert the numbers to the corresponding numeric sets. The mapping guarantees order-preservation and is optimal in the utilization of the numeric space and can perform with very high efficiency. Some application cases are introduced to empirically show the methods advantage which provides a significant reduction of occupied memory and computation overhead compared with the other presentation forms of data. Besides, our mapping is inherently a (Minimal) Perfect hash function so it can inherit applications of the hash functions. The mapping somewhat can be employed as a potential tool in some Big Data applications to consolidate the problems of consumed memory and performance.Author Van Quoc Phuong HuynhUniversität Linz, Dissertation, 2019OeBB(VLID)438010

Crack Identification on the Fresh Chilli (Capsicum) Fruit Destemmed System

Destemming fresh chilli fruit (Capsicum) in large productivity is necessary, especially in the Mekong Delta region. Several studies have been done to solve this problem with high applicability, but a certain percentage of the output consisted of cracked fruits, thus reducing the quality of the system. The manual sorting results in high costs and low quality, so it is necessary that automatic grading is performed after destemming. This research focused on developing a method to identify and classify cracked chilli fruits caused by the destemming process. The convolution neural network (CNN) model was built and trained to identify cracks; then, appropriate control signals were sent to the actuator for classification. Image processing operations are supported by the OpenCV library, while the TensorFlow data structure is used as a database and the Keras application programming interface supports the construction and training of neural network models. Experiments were carried out in both the static and working conditions, which, respectively, achieved an accurate identification rate of 97 and 95.3%. In addition, a success rate of 93% was found even when the chilli body is wrinkled due to drying after storage time at 120 hours. Practical results demonstrate that the reliability of the model was useful and acceptable

Drought and conflicts at the local level: Establishing a water sharing mechanism for the summer-autumn rice production in Central Vietnam

In recent years, water for agricultural production gradually became a significant challenge in the context of climate change in Vietnam. Sustainable solutions are required, which consider the use of resources for both human needs and ecology, and that account for the equitable distribution and the livelihood of the farmers now and in the future. In particular, the farmers in the province of Quang Nam facing water shortage in the cultivation of paddy in the summer-autumn season. Conflicts arise regarding the sharing of the water between the farmers, the drinking water company and the hydropower company. In the context of climate change, the water shortage is expected to increase in the future. The article presents the results of participatory action research (PAR) approach to develop a local level mechanism for water sharing, in which stakeholders actively participated. Water sharing mechanism was developed, envisioning a sustainable solution for inclusive water sharing. The mechanism was successfully implemented in two cases, one at commune level (Tho stream) and one at the district level (Mo stream). The participatory approach proved to be successful in setting up a broadly acceptable mechanism that will need to be further incorporated in the institutional set-up.</p

Synthesis, structure and in vitro cytotoxicity testing of some 2-aroylbenzofuran-3-ols

Five 2-aroyl-5-bromobenzo[b]furan-3-ol compounds (two of which are new) and four new 2-aroyl-5-iodobenzo[b]furan-3-ol compounds were synthesized starting from salicylic acid. The compounds were characterized by mass spectrometry and 1H NMR and 13C NMR spectroscopy. Single-crystal X-ray diffraction studies of four compounds, namely, (5-bromo-3-hydroxybenzofuran-2-yl)(4-fluorophenyl)methanone, C15H8BrFO3, (5-bromo-3-hydroxybenzofuran-2-yl)(4-chlorophenyl)methanone, C15H8BrClO3, (5-bromo-3-hydroxybenzofuran-2-yl)(4-bromophenyl)methanone, C15H8Br2O3, and (4-bromophenyl)(3-hydroxy-5-iodobenzofuran-2-yl)methanone, C15H8BrIO3, were also carried out. The compounds were tested for their in vitro cytotoxicity on the four human cancer cell lines KB, Hep-G2, Lu-1 and MCF7. Six compounds show good inhibiting abilities on Hep-G2 cells, with IC50 values of 1.39-8.03 µM.status: publishe