Reviewerly/exHarmony huggingface dataset:
KCEPS: Knowledge Centric Entity Population Scheme for Research Document Recommendation

The data in the Web has increased exponentially, and retrieval from the Web is quite demanding and challenging. There is always a need to improve results in yielding and recommending scholarly articles. An enhanced recommendation system to recommend scholarly articles is best suited to ease the work of a researcher. It is also used to display relevant articles with respect to the user queries. There is a huge probability that the research article recommended might be of no use to the user if the recommendation algorithm does not perform well. This paper proposes a knowledge centric approach for research paper recommendation using semantic similarity and Gated Recurrent Unit along with Cuttlefish optimization algorithm. The Related-Article Recommendation Dataset is used for experimentation. The recommendation is based on the user query and user clicks. The performance is evaluated and compared with the baseline approaches and it is clearly observed that the proposed Knowledge centric recommendation system is superior in terms of performance and attained an average Accuracy, and F-measure of 96.63%, and 96.69% respectively.
Software Optimization of Rijndael for Modern x86-64 Platforms

The Advanced Encryption Standard (AES) was standardized in 2001 by NIST and has become the de facto block cipher used today. AES is a block cipher with a block size of 128 bits and is based on the proposal by Rijmen and Daemen, named “R i j n d a e l”. The R i j n d a e l proposal includes a definition for a block cipher with 256 bits block size (and a 256-bits key), which we call here R i j n d a e l256. This variant has not been standardized. This paper describes software optimization methods for fast computations of R i j n d a e l256 on modern x86-64 platforms equipped with AES-NI and with vector AES-NI instructions. We explore several implementation methods and report a speed record for R i j n d a e l256 at 0.27 cycles per byte.
Sequential Nature of Recommender Systems Disrupts the Evaluation Process

Datasets are often generated in a sequential manner, where the previous samples and intermediate decisions or interventions affect subsequent samples. This is especially prominent in cases where there are significant human-AI interactions, such as in recommender systems. To characterize the importance of this relationship across samples, we propose to use adversarial attacks on popular evaluation processes. We present sequence-aware boosting attacks and provide a lower bound on the amount of extra information that can be exploited from a confidential test set solely based on the order of the observed data. We use real and synthetic data to test our methods and show that the evaluation process on the MovieLense-100k dataset can be affected by $$\sim $$ 1% which is important when considering the close competition. Codes are publicly available ( https://github.com/alishiraliGit/augmented-boosting-attack ).
Direction-Oriented Topic Modeling with Applications in Traffic Scene Analysis

Unlike text analysis for which topic models are historically developed, traffic video analysis is dealing with much simpler topics, made of restricted motion patterns. In this paper, we propose a dual-layer direction-oriented framework for more efficient traffic motion patterns description based on topic models through considering the simplicity of traffic topics. The aforesaid framework compels the involved topic models to learn the foreknown visually meaningful motion patterns that exist in traffic scenes, as developed theoretically in this paper. Experimental results produced by common datasets show that the proposed method provides more intuitive topics for traffic flow description. Based on experimental results, our framework outperforms other topic-model based methods by 4% to more than 11% in detecting abnormal events, in terms of the AUC. In addition, in a scene analysis evaluation at intersections equipped with traffic signals, our method reaches 4% higher traffic phase detection accuracy, compared to conventional topic models.
Speech Encryption with Fractional Watermark

Research on the feature of speech and image signals are carried out from two perspectives, the time domain and the frequency domain. The speech and image signals are a non-stationary signal, so FT is not used for the non-stationary characteristics of the signal. When short-term stable speech is obtained by windowing and framing the subsequent processing of the signal is completed by the Discrete Fourier Transform (DFT). The Fast Discrete Fourier Transform is a commonly used analysis method for speech and image signal processing in frequency domain. It has the problem of adjusting window size to a for desired resolution. But the Fractional Fourier Transform can have both time domain and frequency domain processing capabilities. This paper performs global processing speech encryption by combining speech with image of Fractional Fourier Transform. The speech signal is embedded watermark image that is processed by fractional transformation, and the embedded watermark has the effect of rotation and superposition, which improves the security of the speech. The paper results show that the proposed speech encryption method has a higher security level by Fractional Fourier Transform. The technology is easy to extend to practical applications.
Mining Frequents Itemset and Association Rules in Diabetic Dataset

Data mining is a field of science to extract and analyses the information from large dataset. One of the most techniques is association rule mining. It aim is to find the relationship between the different attributes of data. Several algorithms for extracting data have been developed. Among the existing algorithms the FP-Growth algorithm is one of well-know algorithm in finding out the desired association rules. The aim of this paper is the extraction of association rules by FP-Growth algorithm and its variants using a diabetic dataset, which are the CFP-Growth and ICFP-Growth. Experimental results show that the ICFP-Growth is more accurate than CFP-Growth and FP-Growth.
A Mixed Reality Application of Francis Turbine Based on HoloLens 2

Francis turbine is the most commonly used turbine today. To better learn the operating principle of the Francis turbine, Mixed Reality is adopted to build a new learning environment. This paper presents a framework of Francis turbine based on HoloLens 2. The novel system has four modules: structure of Francis turbine, the principle of Francis turbine, parts recognition of Francis turbine, and assemble exam of Francis turbine. The modes of student learning behaviors are classified into learning, practice, and evaluation based on the different modules. The proposed learning application occupies a small space, is low in cost, can be used anytime, and ensures the safety of students in the learning process.
Design of a prefixspan algorithm based on prefix position form

PrefixSpan algorithm based on sequence pattern is an association algorithm without generating candidate sequences and only needs to scan the original sequence database twice in the whole operation process. Compared with other sequence algorithms, the PrefixSpan algorithm is more efficient and fast. In this paper, PrefixSpan Base on Prefix Position Form, PPFPrefixSpan is proposed by studying the characteristics of the PrefixSpan algorithm and optimizing it based on it. , which solves the problem that the PrefixSpan algorithm produces too many duplicate projection databases in the process of operation, and optimates the PrefixSpan algorithm in space/time.
Dependence of the Informativity of the Formed Patterns on the Quality of the Initial Data Sample

When solving classification problems using inductive learning methods, problems may arise in the initial data sample, which lead to the building of patterns with low informativity. First of all, these include the presence of a large number of features that describe observations, as well as the presence of outliers in the training sample. To solve the problems under consideration, specially developed algorithmic procedures are proposed that are aimed at preparing the initial data sample for the process of extracting patterns from it with high informativity. A classifier with a high generalizing ability is formed only from highly informative patterns, i.e. high quality classification of new observations. In addition, the paper proposes and tests a heuristic approach for searching for a truncated set of features. Experimental studies are given on a real problem, allowing to determine the feasibility of the proposed algorithmic procedures, which allow solving these problems in the original sample. #CSOC1120.
A Multi-Dimensional Context-Aware Healthcare Service Recommendation Method

Due to the outbreak of the COVID-19, online diagnosis and treatment services have developed rapidly, but it is not easy for patients to choose the appropriate healthcare service in the face of massive amounts of information. This article proposes a multi-dimensional context-aware healthcare service recommendation method, which consists of a healthcare service matching model and a healthcare service ranking model. The former first collects objective knowledge related to doctors and diseases to build a knowledge graph, then matches a group of healthcare services for patients according to the patient’s input; The latter selects 5 indicators from the doctor’s academic level, geographical location, public influence, reputation, etc. to build a TOPSIS model based on the entropy weight method to recommend the most appropriate healthcare services for patients. Finally, taking the patient in Shiyan as an example, the whole process of the method is demonstrated, and the feasibility of the method is verified.
SRSG and S2SG: A Model and a Dataset for Scene Graph Generation of Remote Sensing Images From Segmentation Results

Remote sensing image analysis has drawn more attentions in the field of computer vision. At present, the methods commonly used in remote sensing image analysis are mainly at a low level semantically. The scene graph is an abstraction of objects and their relationships, which is a high level image understanding task. To fully comprehend the meanings of remote sensing images, in this paper, we propose a novel <i>Segmentation-based model to generate Remote sensing image Scene Graphs (SRSG)</i>. In the SRSG model, a more complete and accurate scene graph is generated with the segmentation results as inputs, while the shapes of objects are reasonably coded. The morphological features of object pairs are embedded together by different branches of the SRSG model and then mapped to semantic space to predicate their relationships. Furthermore, a new dataset for scene graph generation of remote sensing images, namely <i>Segmentation results to Scene Graphs (S2SG)</i>, is constructed based on pixel-level segmentation results. Experimental results demonstrate that the performance of the SRSG model is far superior to the previous methods in the task of generating remote sensing image scene graphs. The proposed SRSG model opens up new possibilities for remote sensing image analysis at a high level. Moreover, the S2SG dataset further allows for the evaluation of different approaches and is provided for the benefit of the research community.
Interpreting Convolutional Neural Networks via Layer-Wise Relevance Propagation

Black box characteristics of machine learning algorithms seriously hamper their application in the certain fields, such as medicine, military, finance and so on. So far, the interpretability of machine learning remains as a challenge. In this paper, we use Layer-wise Relevance Propagation (LRP) to calculate the relevance of the Convolutional Neural Network (CNN) on the input data, and visualize it as a heat map, so as to intuitively understand which features the Convolutional Neural Network are based on to make prediction, and then improve the model by analyzing the heat maps. In this article, by using the control variable method, the LRP algorithm is applied to the improved convolution neural network to obtain a new heat map. The difference between the two heat maps is analyzed to verify that the interpretable algorithm conforms to the objective facts. In this way, the interpretability of different network structures is compared and improved.
Low-Medium Level Problems

In this chapter, we present problems of a higher level than the problems of the previous chapter, although we continue with systems with a high degree of abstraction. The main difference is the introduction of binary logical calculations and, therefore, the introduction of logical propositions. In general, the goal is to continue learning modeling techniques.
An Automated Approach for Mapping Between Software Requirements and Design Items: An Industrial Case from Turkey

When a new request comes to the existing software, determining whether there will be reuse and determining where the new requests will be mapped in the existing design are important problems. Since this process is done manually by developers in the context we work, it depends on experience and domain knowledge, besides it is an error-prone and time-consuming process due to the human factor. The main purpose of this study is to correctly predict which new requests in the System Design Document (SDD) match which feature set in the existing software’s Software Requirement Specification (SRS) document. We consider the feature mapping problem between SDD items and SRS requirements as a multi-label multi-class classification problem. Zemberek, a Turkish natural language processing library, is used for preprocessing and feature extraction of the SRS document of the existing software and three SDD documents of different systems to which this software will be delivered. The features extracted from the SRS document are categorized under a certain number of feature topics using the LDA algorithm. The FastText algorithm and AdaBoost-based classifier ICSIBoost are used to decide which of the topics from the SRS document represents a feature in the SDD document, and the predictions are compared with manually determined topics by experts. ICSIBoost achieves quite 67% to 90% precision in topic predictions, whereas the FastText algorithm does not meet our expectations for small and imbalanced data.
A Statistical Recognition System Devise of Negative Transfer of Native Language of Foreign Students Based on Genetic Algorithm

In foreign language acquisition, many errors are inevitable. Negative transfer of native language is one of the most highly significant reasons. Research on the statistical identification of negative transfer of native language of foreign students can minimize the adverse effects of it. This article centers upon the design of a statistical recognition system for the negative transfer of native language of foreign students based on genetic algorithms. It designs and launches a questionnaire survey to analyze the negative transfer of native language in writing, dialogue as well as comprehension in foreign language learning and discusses the reasons and countermeasures for the negative transfer of native language of foreign students. According to survey data, it can conclude that the negative transfer of native language does have an impact on foreign students’ foreign language learning, and this impact cannot be ignored.
Implementation of Real-Time Automated Attendance System Using Deep Learning

In comparison to general manual operations, contemporary technology always saves time and is often more hassle-free when it comes to verifying human authenticity using their biometrical components. However, despite the fact that face recognition technology has been used in a variety of sectors such as human identification systems, this work is the first to describe how the Face Recognition Technique can be integrated with a deep learning approach. Advanced deep learning techniques can make the attendance system completely automated, highly secure, easier to use, and faster to implement than older systems. Nowadays, the Attendance System is becoming increasingly automated, resulting in time-saving, effective, and beneficial solutions that reduce the burden on administration and organizations. In this paper, we suggest an automatic attendance mechanism that is based on Deep Convolutional Neural Networks (DCNN). SeetaFace, a deep convolutional neural network-based face detection system, is employed in this research effort to detect faces in real-time video capture. This implementation is a VIPLFaceNet implementation, to be more specific. AlexNet, which is also a DCNN, is used for image categorization. The experimental results bring four short similarity situations of the classroom such as absence, delayed appearances, early leave, and unauthorized entry during class or session along with the name, student id, and section and passes this information to the attendance sheet which will evaluate the students/persons in the classroom. This methodology saves time when compared to the traditional method of attendance marking, as well as allows organizations to conduct stress-free observations of students and staff.
TextUnet: Text Segmentation Using U-net

Text information extraction from images is a very important field of study as the extracted and correctly interpreted text provides useful semantic meaning to the images they are extracted from. Text extraction from images comprises of three steps: Text Localization, Text Segmentation and Text Recognition. The intermediary step - text segmentation - plays a key role in determining the efficiency of the entire process as the quality of segmentation will directly determine the quality of recognition. Research in the past few years has also stagnated a bit in text segmentation. In this paper we have studied the performance and behaviour of several CNN models based on U-net, that have been trained and tuned for semantic segmentation in born-digital images preprocessed in four different ways - dithering, binarization, edge enhancement and conversion to greyscale. We have also developed a model that outperforms the current topper of the ICDAR 2011 Robust Reading Competition’s Text Segmentation leader board and has an F-score of 83.90% and accuracy of 97.42%.
A Memory-Related Vulnerability Detection Approach Based on Vulnerability Model with Petri Net

With the continuous development of information technology, software vulnerabilities have become a critical threat to information security. Post-release detection of memory leaks, double free and use after free is one of the most challenging research problems in software vulnerability analysis. To tackle this challenge, this paper proposes a Memory-related Vulnerability Detection Approach based on Vulnerability Model (MRVD-VM). Aiming at the above mentioned three memory-related vulnerabilities, we introduce a vulnerability model based on Petri Net that models these three kinds of vulnerabilities. Based on this vulnerability model, we present the framework of MRVD-VM and its vulnerability determination algorithm. A vulnerability detection algorithm (VDA-VM) based on vulnerability model is also studied in the paper. The results of experiments on Juliet Test Suite 1.2 for C_CPP shows that MRVD-VM significantly outperforms three state-of-the-art benchmark tools, including Cppcheck, Flawfinder, and Splint, in detecting memory leaks, double free and use after free.
Learning Video Retrieval Models with Relevance-Aware Online Mining

Due to the amount of videos and related captions uploaded every hour, deep learning-based solutions for cross-modal video retrieval are attracting more and more attention. A typical approach consists in learning a joint text-video embedding space, where the similarity of a video and its associated caption is maximized, whereas a lower similarity is enforced with all the other captions, called negatives. This approach assumes that only the video and caption pairs in the dataset are valid, but different captions - positives - may also describe its visual contents, hence some of them may be wrongly penalized. To address this shortcoming, we propose the Relevance-Aware Negatives and Positives mining (RANP) which, based on the semantics of the negatives, improves their selection while also increasing the similarity of other valid positives. We explore the influence of these techniques on two video-text datasets: EPIC-Kitchens-100 and MSR-VTT. By using the proposed techniques, we achieve considerable improvements in terms of nDCG and mAP, leading to state-of-the-art results, e.g. +5.3% nDCG and +3.0% mAP on EPIC-Kitchens-100. We share code and pretrained models at https://github.com/aranciokov/ranp .
Online Hate Speech Detection on Vietnamese Social Media Texts In Streaming Data

This paper proposes a online hate speech detection system for streaming social media text. A combination of novel preprocessing techniques and state-of-the-art transfer learning model is proposed for social media hate speech detection. The experiment was conducted using the ViHSD dataset. The effectiveness of the proposed preprocessing method is verified as the proposed model achieves a significant improvement over the existing works on ViHDS dataset. An online hate speech detection system that detects and processes online hate speech in real-time was built to consolidate the effectiveness of the proposed system.
COMPARISON ANALYSIS OF THE FUNCTIONS A COMPUTATION OF MODULAR EXPONENTIATION

The computation of the modular exponentiation for big numbers is widely used to find the discrete logarithm, in number-theoretic transforms and in cryptographic algorithms. To efficient compute the modular exponent, new methods, algorithms and means of their implementation are being developed. There are three directions of computational method of modular exponentiation: general modular exponentiation, and computation of the modular exponentiation with a fixed exponent or with a fixed base. Special functions have been developed to perform modular exponentiation in mathematical and cryptographic software libraries. The paper compares the freely available functions of computing the modular exponentiation from the Crypto ++, OpenSSL, Pari / GP and MPIR libraries and developed three functions based on the right-to-left binary shift algorithm. A separate type of numeric data from the MPIR library is used to work with big numbers in the developed functions. The developed functions implement a binary iterative algorithm in one main stream, in two streams and one stream using precomputation. The comparison is based on the average time of execution of the modular exponentiation for pseudo-random data with 1K and 2K bits, which corresponds to the size of about 300 and 600 decimal signs. The runtime results summarized in the table show that the modular exponentiation is computed the fastest by a function from the OpenSSL library, which is almost twice smaller than the function from the Crypto ++ library and three times smaller than the MPIR function in universal computer systems. The implementation of the function of computing the modular exponentiation by mathematical and cryptographic software libraries uses a more optimal modulus multiplication algorithm, the so-called Montgomery multiplication. The developed three functions use multiplication by modulo operations for factors smaller than the module value. The function using precomputation of the remainders for the fixed basis and the module is analyzed separately. After all, in the testing process, the time of precomputation and determination of the periodicity of residues for this function is not taken into account. Further parallelization of the computation of parts of a multi-bit exponent and the use of the Montgomery multiplication algorithm will allow efficient use of the developed function with precomputation for the calculation of the discrete logarithm.