Denial-of-Service Vulnerability of Hash-based Transaction Sharding: Attacks and Countermeasures

Since 2016, sharding has become an auspicious solution to tackle the scalability issue in legacy blockchain systems. Despite its potential to strongly boost the blockchain throughput, sharding comes with its own security issues. To ease the process of deciding which shard to place transactions, existing sharding protocols use a hash-based transaction sharding in which the hash value of a transaction determines its output shard. Unfortunately, we show that this mechanism opens up a loophole that could be exploited to conduct a single-shard flooding attack, a type of Denial-of-Service (DoS) attack, to overwhelm a single shard that ends up reducing the performance of the system as a whole. To counter the single-shard flooding attack, we propose a countermeasure that essentially eliminates the loophole by rejecting the use of hash-based transaction sharding. The countermeasure leverages the Trusted Execution Environment (TEE) to let blockchain's validators securely execute a transaction sharding algorithm with a negligible overhead. We provide a formal specification for the countermeasure and analyze its security properties in the Universal Composability (UC) framework. Finally, a proof-of-concept is developed to demonstrate the feasibility and practicality of our solution

ANALYSIS OF PRESCRIPTION INDICATORS FOR OUTPATIENTS WITH HEALTH INSURANCE IN OUTPATIENTS DEPARTMENT AT CAN THO UNIVERSITY OF MEDICINE AND PHARMACY HOSPITAL IN THE PERIOD 2017-2018

Objective: The main objective of this study was to evaluate the drug prescription parameters and to find out the elements had an influence on the prescribing practice of doctors’. Methods: A descriptive cross-sectional study was conducted to collect 300 outpatient drug prescriptions and 30 questionnaires of physicians during the period of 2017-2018. The data were analyzed according to WHO’s the guideline. Results: Average number of drug per prescription: 3.73, percentage of drugs prescribed by generic or international name (INN): 100%, percentage of prescriptions with an antibiotic prescribed: 24%, of β-lactam antibiotics group, including cephalosporin (31.17%) and aminopenicillin (27.27%), accounted for the highest percentage of using in antibiotic groups with a total of 58.44%, of corticosteroid: 12%, of vitamin: 27.3%, of drugs prescribed including in the Essential Medicines List issued by the Ministry of Health: 35.3%. Average drug cost per prescription: 88,867 VNĐ. Percentage of drug costs for antibiotics (%): 7.48%, of corticosteroids (%): 1.85% and of vitamins (%): 5.25%. Conclusion: The results of this research have identified some prescription indicators and elements affect the prescription indicators such as drug information, patient, drug, which may lead to intervention studies for evaluating changes in these issues in the outpatient clinic

NeuCEPT: Locally Discover Neural Networks' Mechanism via Critical Neurons Identification with Precision Guarantee

Despite recent studies on understanding deep neural networks (DNNs), there exists numerous questions on how DNNs generate their predictions. Especially, given similar predictions on different input samples, are the underlying mechanisms generating those predictions the same? In this work, we propose NeuCEPT, a method to locally discover critical neurons that play a major role in the model's predictions and identify model's mechanisms in generating those predictions. We first formulate a critical neurons identification problem as maximizing a sequence of mutual-information objectives and provide a theoretical framework to efficiently solve for critical neurons while keeping the precision under control. NeuCEPT next heuristically learns different model's mechanisms in an unsupervised manner. Our experimental results show that neurons identified by NeuCEPT not only have strong influence on the model's predictions but also hold meaningful information about model's mechanisms.Comment: 6 main page

XRand: Differentially Private Defense against Explanation-Guided Attacks

Recent development in the field of explainable artificial intelligence (XAI) has helped improve trust in Machine-Learning-as-a-Service (MLaaS) systems, in which an explanation is provided together with the model prediction in response to each query. However, XAI also opens a door for adversaries to gain insights into the black-box models in MLaaS, thereby making the models more vulnerable to several attacks. For example, feature-based explanations (e.g., SHAP) could expose the top important features that a black-box model focuses on. Such disclosure has been exploited to craft effective backdoor triggers against malware classifiers. To address this trade-off, we introduce a new concept of achieving local differential privacy (LDP) in the explanations, and from that we establish a defense, called XRand, against such attacks. We show that our mechanism restricts the information that the adversary can learn about the top important features, while maintaining the faithfulness of the explanations.Comment: To be published at AAAI 202

THE DIFFICULTIES IN ORAL PRESENTATION OF ENGLISH-MAJORED JUNIORS AT TAY DO UNIVERSITY, VIETNAM

It could be broadly accepted that oral presentations are becoming important for students. It is required in almost every field and in the university environment. To succeed in the university environment and in their future jobs, these students need to improve their oral presentation skills. However, one of the drawbacks of using oral presentations in the language classroom is that students often find oral presentations extremely challenging. Therefore, the researcher conducted this research with the main goal of finding out common problems when giving an oral presentation to English-majored juniors at Tay Do University. Ninety juniors majoring in English at Tay Do University were selected to take part in the study. Data are gathered through questionnaires and interviews. The researcher used quantitative and qualitative methods to do the research. From the collected data, when they give a presentation, students usually make some mistakes such as problems in vocabulary, grammar, pronunciation, psychological and background knowledge. Through this study, they would recognize their own problems when giving a presentation. Besides, understanding students' learning difficulties may also enable teachers to help students develop effective learning strategies and ultimately improve their presentation skills. It is hoped that this research can be helpful for not only students but also teachers in learning and teaching English.  Article visualizations

Active Membership Inference Attack under Local Differential Privacy in Federated Learning

Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection through a coordinating server. In this paper, we propose a new active membership inference (AMI) attack carried out by a dishonest server in FL. In AMI attacks, the server crafts and embeds malicious parameters into global models to effectively infer whether a target data sample is included in a client's private training data or not. By exploiting the correlation among data features through a non-linear decision boundary, AMI attacks with a certified guarantee of success can achieve severely high success rates under rigorous local differential privacy (LDP) protection; thereby exposing clients' training data to significant privacy risk. Theoretical and experimental results on several benchmark datasets show that adding sufficient privacy-preserving noise to prevent our attack would significantly damage FL's model utility.Comment: Published at AISTATS 202

STUDY ON THE EFFECT OF CALCIUM-ALGINATE AND WHEY PROTEIN ON THE SURVIVAL RATE OF Bifidobacterium bifidum IN MAYONNAISE

ABSTRACT – QMFS 2019The functional food development by adding probiotic bacteria is getting a lot of concern. In this study, Bifidobacterium bifidum AS 1.1886 was encapsulated in calcium-alginate 2% w/v (C sample) or the mix of calcium-alginate 2% (w/v) and whey protein 1% (w/v) (CW sample) or calcium-alginate 2% (w/v) coated by whey protein 1% (w/v) (CcW sample) by extrusion method, and added to mayonnaise product. The pH changes, the survival rate of probiotic bacteria, and total yeast and mold count during storage, as well as the probiotic survival in simulated gastric medium, were evaluated. The result showed that the pH changes were not significantly different in all mayonnaise samples in this test. The viability of the free probiotic cell was significant decrease about 5.85 log CFU/g compared to 0.26 ÷ 1.14 log CFU/g in encapsulated cell samples after four weeks of storage. None of the free cells survived after six weeks of storage. The total yeast and mold count in samples related to the probiotic count, the viability of probiotic cells higher 6 log CFU/g might be controlling the growth of yeast and molds in mayonnaise. Whey protein has been shown to significantly improve the survival rate of B.bifidum and calcium-alginate coated by whey protein, indicating the most effective protection. The result showed that the application potential of encapsulated probiotic in mayonnaise product

The Predictors of Students’ Satisfaction and Academic Achievements in Online Learning Environment in Higher Education

Student satisfaction is crucial in remote education course evaluation because it is linked to the quality of online programs and student academic performance. Meanwhile, self-regulated learning is crucial in both traditional and online learning environments since it involves the ability to organize, manage, and control their learning process. In this study, the authors tested the correlations between student satisfaction and academic achievement involving student characteristics, self-regulated learning, and Internet self-efficacy. Data were collected from 750 undergraduate students responding to an online survey questionnaire. To examine the correlation between factors in this research, a correlation analysis approach in SPSS 25 was utilized. Qualitative data were coded using MAXQDA in order to figure out other factors affecting student satisfaction. The results of the research showed Internet self-efficacy, self-regulated learning, student satisfaction, and academic achievement were significantly correlated with each other whereas gender and students’ prior experience online were perceived to highly correlate with those constructs as well. Qualitative results indicated factors impacting students’ satisfaction in online learning and supported most part of the quantitative results. Pedagogical implications and limitations of the study are also discussed