Reviewerly/exHarmony huggingface dataset:
IBV-CFI: Efficient fine-grained control-flow integrity preserving CFG precision

Control-flow integrity (CFI) is a software security solution that prevents software attacks such as control-flow hijacking by restricting the indirect control-flow transfers (ICT) to a pre-computed control-flow graph (CFG). Since the validity of ICTs are determined based on CFG on the CFI mechanism, CFG precision is an important factor in determining CFI security level. However, checking the validity of ICTs based on a precise CFG can incur significant runtime overhead. For this reason, many existing CFI schemes have used a runtime check mechanism that compromises the precision of the CFG. In this paper, we present an Index-based Bit Vector Control-Flow Integrity scheme (IBV-CFI), which performs an efficient runtime check while preserving CFG precision. IBV-CFI generates independent bit vectors for all ICTs and stores a valid target set for each ICT in the bit vector. Independent bit vectors accurately reflect the CFG, so they do not compromise the precision of CFG. In addition, it is possible to determine the validity of the target of the indirect branch through a simple bit value comparison, which enables an efficient runtime check. We implemented a prototype model, IBV-CFI, and performed performance measurements using the SPEC CPU 2017 benchmarks and three real-world applications. The results show that IBV-CFI introduces approximately 1.42% performance overhead.
Uniformly Interpolated Balancing for Robust Prediction in Translation Quality Estimation

There has been growing interest among researchers in quality estimation (QE), which attempts to automatically predict the quality of machine translation (MT) outputs. Most existing works on QE are based on supervised approaches using quality-annotated training data. However, QE training data quality scores readily become imbalanced or skewed : QE data are mostly composed of high translation quality sentence pairs but the data lack low translation quality sentence pairs. The use of imbalanced data with an induced quality estimator tends to produce biased translation quality scores with “high” translation quality scores assigned even to poorly translated sentences. To address the data imbalance, this article proposes a simple, efficient procedure called uniformly interpolated balancing to construct more balanced QE training data by inserting greater uniformness to training data. The proposed uniformly interpolated balancing procedure is based on the preparation of two different types of manually annotated QE data: (1) default skewed data and (2) near-uniform data . First, we obtain default skewed data in a naive manner without considering the imbalance by manually annotating qualities on MT outputs. Second, we obtain near-uniform data in a selective manner by manually annotating a subset only, which is selected from the automatically quality-estimated sentence pairs. Finally, we create uniformly interpolated balanced data by combining these two types of data, where one half originates from the default skewed data and the other half originates from the near-uniform data. We expect that uniformly interpolated balancing reflects the intrinsic skewness of the true quality distribution and manages the imbalance problem. Experimental results on an English-Korean quality estimation task show that the proposed uniformly interpolated balancing leads to robustness on both skewed and uniformly distributed quality test sets when compared to the test sets of other non-balanced datasets.
Research on the Design and Application of Implicit Semantic Model in Bundle Recommendation Method

Abstract The recommendation system has been studied for a long time. Data of more users and commodities has been used to improve precision of the recommendation system. However, few studies are on utilization of bundling information. The bundling information refers to the inherent connection of the commodities, and depended by objective properties of commodities. This paper adds bundling information to the recommendation method based on traditional implicit semantic model and collaborative filtering method. This paper sets the bundling coefficient to show the connection degree between commodities and recommended decisive factors. Experiment shows the precision of recommendation could be improved with bundling information in specific environment through changing bundling coefficient.
Image Quality Enhancement with 2<sup>k</sup>-bit Correction in Pixel Repetition Embedding

While a higher embedding capacity of an RDH scheme is a timely need for the recent imaging-based applications, it proportionately degrades the visual quality of an embedded image. Existing RDH schemes have no separate consideration of improving the embedded image quality. This paper presents a new approach to improving the embedded image quality for a high capacity RDH scheme by utilizing a simple 2k -bit correction. A Pixel-Repetition (PR) is used to create an image block of 2 × 2 size for each of the original pixel (that we call a seed) of the image. Unlike the previous PR-based schemes, the embedding conditions are redefined to regenerate the seed pixel from the other 2 pixels of the block to embed additional k-bits in the seed pixel and apply the 2k -bit correction to ensure a better-embedded image quality. The preliminary results demonstrate that the proposed pixel correction significantly improves the embedded image quality at the higher embedding rate compared to the other promising RDH schemes.
SOUA: Towards Intelligent Recommendation for Applying for Overseas Universities

In China, to pursue a master's degree in foreign countries is becoming an increasingly popular choice for undergraduate students. However, predicting the results of university applications is mostly done by applying empirical rules and referring to previous admission cases similar to the applicant's conditions, which is unreliable. In this paper, we propose an intelligent recommendation method, called SOUA, to make recommendations for students who apply for the admissions of overseas universities to study abroad. Our method is proposed based on a dataset obtained by us, which is about Chinese undergraduate students' applying for master's degrees in computer science-related majors of American universities. We apply data mining techniques on this dataset. A new data augmentation method named ILGA is proposed to expand the size of the training set. In the experiments, machine learning models such as LR, KNN, DT, SVM, RF, GBDT and MLP are investigated using this dataset. The experimental results show that RF achieving a weighed f1-score of 71.2% on the test set outperforms other considered machine learning models. And RF produces the optimal university recommendation accuracy of 80.9%.
Object Detection Based on Minimum Convex Hull and Generalized Hough Transform

Abstract In view of the low efficiency and fallibility of the feature-based matching algorithm in object detection, a new algorithm based on convex hull region and generalized hough transform is proposed.By using the region of interest to narrow the detection region and applying the key feature points to the voting elements of the generalized hough transform, the computation is greatly reduced.Firstly, the fused key feature points are extracted and filtered to generate the convex hull region which is combined with the GBR clustering algorithm to determine the region of interest. Secondly, the key feature points are taken as the voting elements in the generalized hough transform algorithm to obtain the final result. In this paper, dozens of various transformation experiments(scaling, rotation, noise, etc.) based on the proposed algorithm are compared with other similar methods, and it is verified that the method has higher efficiency and accuracy according to the theoretical basis and concrete implementation, which can meet the real-time and high-precision requirements in industrial detection.
Species-Based Differential Evolution with Migration for Multimodal Optimization

Due to the fact that many realistic problems are multimodal optimization problems (MMOPs), multimodal optimization has attracted a growing interest. In this paper, a Species-based differential evolution with migration algorithm is proposed for MMOPs. First, a migration strategy is developed to improve the exploration ability of species with less individuals. Then, an improved operation of creating local individuals is introduced to help the algorithm to find high-quality solutions. Finally, an archive mechanism is applied to preserve the best solutions found during previous generations and avoid the loss of the peaks. Experimental results on CEC2013 test problems confirm the effectiveness of the proposed algorithm compared to several well-known multimodal optimization algorithms.
Similar Cluster Based Continuous Bag-of-Words for Word Vector Training

With the increasing use of natural language processing, how to build a word vector which contains more semantic information becomes a top priority. Word vector is used to represent the most basic unit in the language-word, and is the basis of the neural natural language processing model. Therefore the quality of word vectors directly affects the performance of various applications. In continuous bag-of-words model, limited by the frequency of occurrence, some words do not get enough training. At the same time, based on the set of minimum frequency, some low-frequency words are ignored by the model. In this paper, we build similar clusters from the semantic dictionary and integrate it into CBOW model with the help of multi-classifier. We improve word vectors and use it to complete a semantic similarity comparison task. Compared with the original word vectors built by CBOW, the method we proposed got higher accuracy. It shows some semantic information are integrated and the word vectors of low–frequency words are improved.
Human Perception Evaluation toward End of File Steganography Method’s Implementation Using Multimedia File (Image, Audio, and Video)

Data exchange occurs with a variety of purposes, ranging from sharing information or just basic communication between people. Data exchanges that occur every day make data traffic busier. Data theft is made even easier by utilizing a large number of message senders and busy data traffic. One popular method used to secure data is the steganography method. In this research, the End of File steganography method is successfully implemented in various types of multimedia, namely image, audio, and video. The steganography process is done using a variety of secret message sizes, then some evaluations are carried out. In addition, the purpose of the evaluation is also to find out the comparative advantages between the types of media used. Evaluations that done for this research included changes in size, changes in physical cover files, histograms, and Mean of Score assessments. From all of the evaluations, it can be concluded that the most suitable multimedia files to be used as steganography media for the End of File method are the image and video media. However, this method provides the effect of increasing the size, so it is advisable to use video because generally, the video size is always larger than the image size because it contains images and audio in one file at a time.
Design of an Ontology Model Based Ship Condition Monitoring Middleware

It is important for the ship automation management to implement the ship condition monitoring and real-time fault early warning. In order to improve the efficiency of data collection and intelligence of ship monitoring, a ship monitoring ontology model is proposed by using Web Ontology Language (OWL) and a reason manager is designed by using Semantic Web Rule Language (SWRL). Besides, Java Expert Shell System (JESS) based reason machine is implemented to integrate the ship condition monitoring ontology and inference rules. A test bed is developed to verify the performance of the middleware system with real-time sensor data. The results show that the proposed middleware can improve the accuracy and decrease the warning delay compared with existed traditional monitoring system.
A Direction-Based Exponential Crossover Operator for Real-Coded Genetic Algorithm

In this paper, a new direction-based exponential crossover operator (DEX) for a real-coded genetic algorithm (RCGA) has been developed. Its name indicates that this newly designed DEX is influenced by the directional knowledge of the problem. This knowledge about a problem actually helps to decide the search direction of the algorithm in the variable space to move toward the globally optimum solution. Now, the task of collecting this data is quite tricky and may have several ways. However, we suggest one approach to obtain this knowledge during the evolution of solutions. Utilizing this prior knowledge during the crossover operation, the children solutions are created with a biasness of that search direction. This, in fact, makes the searching mechanism of an RCGA more efficient. To measure the performance of the DEX, ten classical benchmark test functions have been taken and the experiments are done using an RCGA with the proposed crossover operator, and the results are compared with another popular crossover operator, namely, simulated binary crossover (SBX).
<i>DynamiQue</i> – A Technical Intervention To Augment Static Textbook with Dynamic Q&amp;A

E-books have become more prevalent in the present day, due to their availability and numerous advantages over textbooks, and more so in the domain of computer science and programming. On the other hand, learners also use Q&A websites such as Stack Overflow as these websites provides a platform for asking and answering questions in communication with the developer community. Even though learning programming requires dynamic content, the current e-books are observed to be static in nature. To address this challenge, we propose an approach to overlay related Q&As throughout the e-book, bringing dynamic content into a static textbook. The approach consists in summarizing different parts of the text, identifying core keywords from the text, finding related core Q&As from Stack Overflow and finally integrating Q&As into the e-book. To achieve this, we take an e-book as input, apply Latent Dirichlet Allocation and generate augmented e-book with relevant Q&As. We have evaluated DynamiQue prototype with 30 volunteers in the age group of 18–25 years using a likert scale-based questionnaire. The initial results are promising with all the questions having a mean score greater than 3.4. Volunteers also suggested the scope to improve user interface as well as the relevance of Q&As.
Water Wave Optimization with Self-adaptive Directed Propagation

Water wave optimization (WWO) is a recently proposed nature-inspired algorithm that mimics shallow water wave motions to solve optimization problems. In this paper, we propose an improved WWO algorithm with a new self-adaptive directed propagation operator, which dynamically adjusts the propagation direction of each solution according to the fitness change caused by the last propagation operation to improve local search ability. The new algorithm also adopts a nonlinear population reduction strategy to better balance the local search and global search abilities. Experimental results on 15 function optimization problems from the CEC2015 single-objective optimization test suite show that the improved algorithm exhibits significantly better performance than the original WWO and some other evolutionary algorithms including particle swarm optimization (PSO) and biogeography-based optimization (BBO), which validates the effectiveness and efficiency of the proposed strategies.
Implementation of 2DPCA and SOM Algorithms to Determine Sex According to Lip Shapes

Lips are part of body parts in facial area which are unique in shapes and may have different patterns. Pattern or shape of lips can be used as an individual identity, either for a live or deceased person. Forensic science is a branch of medical science which utilizes lip shape to determine the identity of a person, including their sex. Determination of sex using image of lip shape using 2DPCA and SOM algorithms was successful in classifying the sex of a person, as male or female, using desktop-based Java programming language. This study employed 90 lip images, consisting of 60 training data (30 of male and 30 of female) and 30 testing data of male and female. The testing result showed accuracy rate of 76,66% with a rate of 0,9 and maximum iteration of 10000.
An Improved Adversarial Neural Network Encryption Algorithm Against the Chosen-Cipher Text Attack (CCA)

Password attacks are classified into four types: known-plain text attack, cipher text attack, chosen-plain text attack, and chosen-cipher text attack. The chosen-cipher text attack is the strongest attacking mode among the four attack modes. In order to resist the most aggressive chosen-cipher text attack mode, this paper proposes an improved adversarial neural network encryption method. We call it CCA-ANC encryption algorithm which changes the attack mode of the attacker to use the chosen-cipher text attack (CCA) method that can give the attacker a stronger cracking capability so as to force both encryption and decryption to use a better encryption system generating a more secure encryption algorithm.
Toward FAIRness and a User-Friendly Repository for Supporting NMR Data

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTToward FAIRness and a User-Friendly Repository for Supporting NMR DataBarbara C. Sorkin*Barbara C. SorkinU.S. National Institutes of Health, Bethesda, Maryland 20892, United States*Email: [email protected]More by Barbara C. Sorkin, Joseph M. BetzJoseph M. BetzU.S. National Institutes of Health, Bethesda, Maryland 20892, United StatesMore by Joseph M. Betz, and D. Craig HoppD. Craig HoppU.S. National Institutes of Health, Bethesda, Maryland 20892, United StatesMore by D. Craig HoppCite this: J. Org. Chem. 2020, 85, 8, 5131Publication Date (Web):April 3, 2020Publication History Published online3 April 2020Published inissue 17 April 2020https://doi.org/10.1021/acs.joc.0c00800Copyright © 2020 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views1255Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (447 KB) Get e-AlertsSUBJECTS:Dietary supplements,Nuclear magnetic resonance spectroscopy,Organic chemistry Get e-Alerts
An Adaptive Scaling Factor for Multiple Watermarking Scheme

This paper presents an adaptive scaling factor for multiple watermarking scheme. Watermarks are inserted into the red and green channels with lowest variance of the cover image. DCT is applied on each selected block for transforming image pixels to DCT coefficients. This experiment examines DCT coefficients in the middle frequency to obtain adaptive scaling factor for embedding a watermark. The proposed algorithm used the impact of selected DCT coefficients to imperceptibility and robustness for generating adaptive scaling factor. Arnold transform is used to provide additional security and secrecy of the embedded watermark image. The experimental results reported that our scheme achieves higher invisibility performance for multiple watermarks compared to existing schemes.
Dialog Acts Classification with Semantic and Structural Information

Dialog act, which is an essential element in dialog systems, plays an important role in understanding users' intents in the human-computer dialog. With the increasing applications of deep learning in natural language processing, more and more researchers adopt methods of text classification to identify dialog acts. However, sometimes one utterance may correspond to multiple labels, which leads to a cumbersome problem in classifying dialog acts. In this paper, we propose a multi-label classification model for solving this issue. Meanwhile, as Graph Convolutional Network can extract spatial features of topological graphs, we utilize it to capture syntactic information to enrich the words' semantic representations. In addition, due to the fact that one utterance consists of different clauses and each clause may correspond to one act label, we associate the relationship between clauses through Recurrent Neutral Network. Finally, our model combines the above two-level representations together. Compared with other classification models, experimental results can verify that our proposed model performs better.
On the linear structures of balanced functions and quadratic APN functions

The set of linear structures of most known balanced Boolean functions is non-trivial. In this paper, some balanced Boolean functions whose set of linear structures is trivial are constructed. We show that any APN function in even dimension must have a component whose set of linear structures is trivial. We determine a general form for the number of bent components in quadratic APN functions in even dimension and some bounds on the number are produced. We also count bent components in any quadratic power functions.
Evaluating the Usability of Heuristics within Telegram using the Linear Regression Method

Telegram is a cloud-based instant messaging application that focuses on speed and security. Telegram is designed to make it easy for users to send text messages, audio, video, images and stickers to each other safely. Telegram is growing rapidly and the application has been downloaded more than 100 million times. Although it has been downloaded more than 100 million times in reviews on the Google Play Store some users complain about the Telegram application. The problem is part of the usability heuristic interface aesthetic and minimalist design problem that exists in telegram applications. Because of this, it is necessary to explore more comprehensive problems to better explore the problems that occur in telegram application users. The usability heuristic level needs to be known to explain its effectiveness in telegram applications for users. This study provides evaluation results to determine the level of usability heuristic in telegram applications using multiple linear regression methods. The results obtained from this study indicate that 5 usability heuristic variables exist in telegram applications according to the user.