Does BLEU Score Work for Code Migration?

Statistical machine translation (SMT) is a fast-growing sub-field of computational linguistics. Until now, the most popular automatic metric to measure the quality of SMT is BiLingual Evaluation Understudy (BLEU) score. Lately, SMT along with the BLEU metric has been applied to a Software Engineering task named code migration. (In)Validating the use of BLEU score could advance the research and development of SMT-based code migration tools. Unfortunately, there is no study to approve or disapprove the use of BLEU score for source code. In this paper, we conducted an empirical study on BLEU score to (in)validate its suitability for the code migration task due to its inability to reflect the semantics of source code. In our work, we use human judgment as the ground truth to measure the semantic correctness of the migrated code. Our empirical study demonstrates that BLEU does not reflect translation quality due to its weak correlation with the semantic correctness of translated code. We provided counter-examples to show that BLEU is ineffective in comparing the translation quality between SMT-based models. Due to BLEU's ineffectiveness for code migration task, we propose an alternative metric RUBY, which considers lexical, syntactical, and semantic representations of source code. We verified that RUBY achieves a higher correlation coefficient with the semantic correctness of migrated code, 0.775 in comparison with 0.583 of BLEU score. We also confirmed the effectiveness of RUBY in reflecting the changes in translation quality of SMT-based translation models. With its advantages, RUBY can be used to evaluate SMT-based code migration models.Comment: 12 pages, 5 figures, ICPC '19 Proceedings of the 27th International Conference on Program Comprehensio

Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network

The process of damage identification in Structural Health Monitoring (SHM) gives us a lot of practical information about the current status of the inspected structure. The target of the process is to detect damage status by processing data collected from sensors, followed by identifying the difference between the damaged and the undamaged states. Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. In this paper, a novel method of structural damage detection is proposed using convolution neural network and recurrent neural network. A convolution neural network is used to extract deep features while recurrent neural network is trained to learn the long-term historical dependency in time series data. This method with combining two types of features increases discrimination ability when compares with it to deep features only. Finally, the neural network is applied to categorize the time series into two states - undamaged and damaged. The accuracy of the proposed method was tested on a benchmark dataset of Z24-bridge (Switzerland). The result shows that the hybrid method provides a high level of accuracy in damage identification of the tested structure

Effective Feature Extraction Method for SVM-Based Profiled Attacks

Nowadays, one of the most powerful side channel attacks (SCA) is profiled attack. Machine learning algorithms, for example support vector machine, are currently used for improving the effectiveness of the attack. One issue when using SVM-based profiled attack is extracting points of interest, or features from power traces. So far, studies in SCA domain have selected the points of interest (POIs) from the raw power trace for the classifiers. Our work proposes a novel method for finding POIs that based on the combining variational mode decomposition (VMD) and Gram-Schmidt orthogonalization (GSO). That is, VMD is used to decompose the power traces into sub-signals (modes) of different frequencies and POIs selection process based on GSO is conducted on these sub-signals. As a result, the selected POIs are used for SVM classifier to conduct profiled attack. This attack method outperforms other profiled attacks in the same attack scenario. Experiments were performed on a trace data set collected from the Atmega8515 smart card run on the side channel evaluation board Sakura-G/W and the data set of DPA contest v4 to verify the effectiveness of our method in reducing number of power traces for the attacks, especially with noisy power traces

Multi-level damage detection using a combination of deep neural networks

In recent years, bridge damage identification using a convolutional neural network (CNN) has become a hot research topic and received much attention in the field of civil engineering. Although CNN is capable of categorizing damaged and undamaged states from the measured data, the level of accuracy for damage diagnosis is still insufficient due to the tendency of CNN to ignore the temporal dependency between data points. To address this problem, this paper introduces a novel hybrid damage detection method based on the combination of CNN and Long Short-Term Memory (LSTM) to classify and quantify different levels of damage in the bridge structure. In this method, the CNN model will be used to extract the spatial damage features, which will be combined with the temporal features obtained from Long Short-Term Memory (LSTM) model to create the enhanced damage features. The combination successfully strengthened the damage detection capability of the neural network. Moreover, deep learning is also improved in this paper to process the acceleration-time data, which has a different amplitude at short intervals and the same amplitude at long intervals. The empirical result on the Vang bridge shows that our hybrid CNN-LSTM can detect structural damage with a high level of accuracy

New primitives of controlled elements F2/4 for block ciphers

This paper develops the cipher design approach based on the use of data-dependent operations (DDOs). A new class of DDO based on the advanced controlled elements (CEs) is introduced, which is proven well suited to hardware implementations for FPGA devices. To increase the hardware implementation efficiency of block ciphers, while using contemporary FPGA devices there is proposed an approach to synthesis of fast block ciphers, which uses the substitution-permutation network constructed on the basis of the controlled elements F2/4 implementing the 2 x 2 substitutions under control of the four-bit vector. There are proposed criteria for selecting elements F2/4 and results on investigating their main cryptographic properties. It is designed a new fast 128-bit block cipher MM-128 that uses the elements F2/4 as elementary building block. The cipher possesses higher performance and requires less hardware resources for its implementation on the bases of FPGA devices than the known block ciphers. There are presented result on differential analysis of the cipher MM-12

A rare case report on bilateral scrotal lipoma — the largest tumor in Vietnam

Scrotal lipoma is benign and still, one of the rarest cancers, with very few cases previously reported in the world. The exact pathogenesis of lipomas remains unknown. Scrotal lipomas can be classified into three categories based on their origins: scrotal lipoma, spermatic cord and tunica vaginalis tumor, and primary scrotal lipoma. The disease may be misdiagnosed or diagnosed inaccurately. We present a case of a 46-year-old male with a giant bilateral scrotal lipoma presenting as scrotal swelling and discomfort, which was first diagnosed as an inguinal hernia. Computed tomography, ultrasound, and fine needle aspiration were performed and aroused a suspicion of lipoma. An operation was performed, and the tumor was completely excised and histologically confirmed as a lipoma. To our knowledge, this is the largest scrotal tumor reported in Vietnam, which led to not only diagnostic but also treatment challenges. Therefore, it is significant to report similar cases that can help clinicians diagnose and handle such tumors in a timely manner

Limonene and eucalyptol rich essential oils with their antimicrobial activity from the leaves and rhizomes of Conamomum vietnamense N.S. Lý & T.S. Hoang (Zingiberaceae)

Conamomum vietnamense, a new species of Zingiberaceae family, has been discovered and described from Tay Nguyen (Central Highlands, Vietnam) in 2022. The present study described the preparation of essential oil from leaves and rhizomes of the plant by hydrodistillation process. Then, the chemical composition of these essential oils was analyzed by Gas Chromatography-Mass Spectrometry (GC-MS), which indicated that limonene (18.74 and 26.20%) and eucalyptol (40.47 and 49.49%) were the main components, respectively. The essential oils also showed moderate antimicrobial activities against Gram-positive bacterial strains (Enterococcus faecalis ATCC 299212, Staphylococcus aureus ATCC 25923, Bacillus cereus ATCC 14579), Gram-negative bacterial strains (Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853, Salmonella enterica ATCC 13076), and a pathogenic yeast (Candida albicans ATCC 10231) in the MIC range of 32–256 μg/mL, which was comparable to those of positive controls, streptomycin and cycloheximide. For the first time, the chemical composition and antimicrobial activity of the essential oil of C. vietnamense were studied

Damage detection in structural health monitoring using combination of deep neural networks

Structural Health Monitoring is a process of continuous evaluation of infrastructure status. In order to be able to detect the damage status, data collected from sensors have to be processed to identify the difference between the damaged and the undamaged states. In recent years, convolution neural network has been applied to detect the structural damage and with positive results. This paper proposes a new method of damage detection using combination of deep neural networks. The method uses a convolution neural network to extract deep features in time series and Long Short Term Memory network to find a statistically significant correlation of each lagged features in time series data. These two types of features are combined to increase discrimination ability compared to deep features only. Finally, the fully connected layer will be used to classify the time series into normal and damaged states. The accuracy of damaged detection was tested on a benchmark dataset from Los Alamos National Laboratory and the result shows that hybrid features provided a highly accurate damage identification