Reviewerly/exHarmony huggingface dataset:
Cross-Domain Local Characteristic Enhanced Deepfake Video Detection

As ultra-realistic face forgery techniques emerge, deepfake detection has attracted increasing attention due to security concerns. Many detectors cannot achieve accurate results when detecting unseen manipulations despite excellent performance on known forgeries. In this paper, we are motivated by the observation that the discrepancies between real and fake videos are extremely subtle and localized, and inconsistencies or irregularities can exist in some critical facial regions across various information domains. To this end, we propose a novel pipeline, Cross-Domain Local Forensics (XDLF), for more general deepfake video detection. In the proposed pipeline, a specialized framework is presented to simultaneously exploit local forgery patterns from space, frequency, and time domains, thus learning cross-domain features to detect forgeries. Moreover, the framework leverages four high-level forgery-sensitive local regions of a human face to guide the model to enhance subtle artifacts and localize potential anomalies. Extensive experiments on several benchmark datasets demonstrate the impressive performance of our method, and we achieve superiority over several state-of-the-art methods on cross-dataset generalization. We also examined the factors that contribute to its performance through ablations, which suggests that exploiting cross-domain local characteristics is a noteworthy direction for developing more general deepfake detectors.
UTB180: A High-Quality Benchmark for Underwater Tracking

Deep learning methods have demonstrated encouraging performance on open-air visual object tracking (VOT) benchmarks, however, their strength remains unexplored on underwater video sequences due to the lack of challenging underwater VOT benchmarks. Apart from the open-air tracking challenges, videos captured in underwater environments pose additional challenges for tracking such as low visibility, poor video quality, distortions in sharpness and contrast, reflections from suspended particles, and non-uniform lighting. In the current work, we propose a new Underwater Tracking Benchmark (UTB180) dataset consisting of 180 sequences to facilitate the development of underwater deep trackers. The sequences in UTB180 are selected from both underwater natural and online sources with over 58,000 annotated frames. Video-level attributes are also provided to facilitate the development of robust trackers for specific challenges. We benchmark 15 existing pre-trained State-Of-The-Art (SOTA) trackers on UTB180 and compare their performance on another publicly available underwater benchmark. The trackers consistently perform worse on UTB180 showing that it poses more challenging scenarios. Moreover, we show that fine-tuning five high-quality SOTA trackers on UTB180 still does not sufficiently boost their tracking performance. Our experiments show that the UTB180 sequences pose a major burden on the SOTA trackers as compared to their open-air tracking performance. The performance gap reveals the need for a dedicated end-to-end underwater deep tracker that takes into account the inherent properties of underwater environments. We believe that our proposed dataset will be of great value to the tracking community in advancing the SOTA in underwater VOT. Our dataset is publicly available on Kaggle .
HAZE-Net: High-Frequency Attentive Super-Resolved Gaze Estimation in Low-Resolution Face Images

Although gaze estimation methods have been developed with deep learning techniques, there has been no such approach as aim to attain accurate performance in low-resolution face images with a pixel width of 50 pixels or less. To solve a limitation under the challenging low-resolution conditions, we propose a high-frequency attentive super-resolved gaze estimation network, i.e., HAZE-Net. Our network improves the resolution of the input image and enhances the eye features and those boundaries via a proposed super-resolution module based on a high-frequency attention block. In addition, our gaze estimation module utilizes high-frequency components of the eye as well as the global appearance map. We also utilize the structural location information of faces to approximate head pose. The experimental results indicate that the proposed method exhibits robust gaze estimation performance even in low-resolution face images with 28 $$\times $$ 28 pixels. The source code of this work is available at https://github.com/dbseorms16/HAZE_Net/ .
CLUE: Consolidating Learned and Undergoing Experience in Domain-Incremental Classification

Deep neural networks tend to be vulnerable to catastrophic forgetting when learning new tasks. To address it, continual learning has become a promising and popular research field in recent years. It is noticed that plentiful research predominantly focuses on class-incremental (CI) settings. However, another practical setting, domain-incremental (DI) learning, where the domain distribution shifts in new tasks, also suffers from deteriorating rigidity and should be emphasized. Concentrating on the DI setting, in which the learned model is overwritten by new domains and is no longer valid for former tasks, a novel method named Consolidating Learned and Undergoing Experience (CLUE) is proposed in this paper. In particular, CLUE consolidates former and current experiences by setting penalties on feature extractor distortion and sample outputs alteration. CLUE is highly applicable to classification models as neither extra parameters nor processing steps are introduced. It is observed through extensive experiments that CLUE achieves significant performance improvement compared with other baselines in the three benchmarks. In addition, CLUE is robust even with fewer replay samples. Moreover, its feasibility is supported by both theoretical derivation and model interpretability visualization. The code is available at: https://github.com/Multiplied-by-1/CLUE .
What Proportion of the Bitcoin Ecosystem Is Controlled by Bots?

Its proponents argue that Bitcoin allows for disruption of how money transfers are made. They also claim that Bitcoin is less susceptible to fraud than money because the BTC balance cannot be duplicated and used by another person. Its critics claim it is a bubble because of its high volatility and the number of security breaches. People who create bitcoin or other cryptocurrencies care because the use of bots means their technology is less trusted and valuable. Also, people who sell assets that rely on Bitcoin price stability (such as bitcoin futures and options) care because bot activity can cause price fluctuations and financial losses. This research aims to study how to control and regulate the influence of Bitcoin bots. Our market analysis reveals unusual trading recently that was most likely triggered by some significant event. This finding is consistent with our extensive robustness checks.
EFL Vocabulary Acquisition through Reading Courses during Covid-19

This work aimed to explain how students at the College of Languages and Translation (CLT) managed to learn vocabulary through reading courses during the Covid-19 Pandemic. To this end, the researcher adopted a descriptive approach and collected the data mainly through a questionnaire. The sample included students of English at CLT, Imam Mohammad Ibn Saud Islamic University (IMSIU). Teachers and students found themselves forced to shift from traditional to virtual classes, so they resorted to another type of teaching that generally relied on online instruction. The findings showed that there were some difficulties initially, moving from traditional to virtual classes, where the students had to use their previous skills to acclimatize to the shift. The findings also showed that the teaching tools were not adequately available to fit the purpose of vocabulary learning. The researcher suggested using modern vocabulary apps such as Vocab Genius, WordUp, Vocabology, Vocabulary Builder, and Vocabulary App. Keywords: Active Vocab, MALL, Online Instruction, Passive Vocab.
Weighted Contrastive Hashing

The development of unsupervised hashing is advanced by the recent popular contrastive learning paradigm. However, previous contrastive learning-based works have been hampered by (1) insufficient data similarity mining based on global-only image representations, and (2) the hash code semantic loss caused by the data augmentation. In this paper, we propose a novel method, namely Weighted Contrative Hashing (WCH), to take a step towards solving these two problems. We introduce a novel mutual attention module to alleviate the problem of information asymmetry in network features caused by the missing image structure during contrative augmentation. Furthermore, we explore the fine-grained semantic relations between images, i.e., we divide the images into multiple patches and calculate similarities between patches. The aggregated weighted similarities, which reflect the deep image relations, are distilled to facilitate the hash codes learning with a distillation loss, so as to obtain better retrieval performance. Extensive experiments show that the proposed WCH significantly outperforms existing unsupervised hashing methods on three benchmark datasets. Code is available at: http://github.com/RosieYuu/WCH .
Early Risk Prediction of Breast Cancer among Patients using Machine Learning Techniques

Breast cancer is still the leading cause of mortality among women today. The purpose of the current investigation is to properly forecast the risk of acquiring breast cancer. To forecast the risk based on gene expression data, A variety of machine learning methods may be utilized to help. When it comes to interpreting gene expression data, The most often encountered difficulty is determining which genes are the most informative. To maintain good health, every progress in cancer sickness diagnosis and prediction is essential. Since cancer treatment choices and survival rates are always evolving, A high degree of accuracy in cancer prediction is essential. Breast cancer prediction and early diagnosis may be considerably improved by ML approaches, which have developed a research hotspot & have been demonstrated to be a highly useful technology in the area of breast cancer. In this research, 3 algorithms are presented, one of which is a machine learning algorithm, which has the highest accuracy of the three algorithms analyzed. Using these algorithms, we can detect breast cancer at an early stage.
Zero-Text Watermarking Method for Embedded of Watermark Originality

In this era of technology, the ways to share information among people are completely changed. Technology made it fast to connect with people and share their views within second by using different social sites. The need for hours is how to secure data in a more precise form that can survive by any unauthorized users' raids such as copy, changing, insertion, and deletion of data. These types of attacks destroyed the originality of data. Data may be in any contents shape audio, video, and text but text contents are most difficult to survive from unauthorized access and use. As the content of the text is sharing in soft form over different social sites so the chances of attacks are increasing and the value of the text is difficult to maintain. The purposed solution of securing information or text from unauthorized attacks is text watermarking. Watermarking is the most host topic due to increasing attacks and as the technology is growing. To secure confidential information is one of the necessities of this era for preserving the originality and authentication of contents. In recent years different techniques of text watermarking are using for the security purposes of text. Different techniques and algorithms are used but the main requirements of text watermarking are not successfully achieved. These techniques may not give full access to attackers but the value of the text is degraded. This thesis presents a more enhanced technique to maintain the originality of the text.
Comparative Analysis of Machine Learning Techniques for Breast Tumor Classification on Balanced and Unbalanced Datasets

Early detection of breast cancer can significantly help in reducing the death rate due to the same. With the advancement in computer aided diagnosis, a trend of improving the diagnostic accuracy can be seen. In this work, multiple research papers are reviewed and it is concluded that optimum accuracy could not be achieved due to the use of unbalanced or small datasets. To resolve these problems, two publicly available databases are combined: Breast Cancer Histopathological Image Classification Dataset and Dataset of Breast Ultrasound Images resulting in a balanced dataset consisting of 1099 malignant and 1113 benign images. Then, a variety of machine learning techniques are used with hyperparameter tuning using GridSearchCV for binary classification of ultrasound images of breasts tumors into malignant and benign classes. A comparative analysis of the implemented techniques is performed on both the datasets, Dataset of Breast Ultrasound Images (unbalanced) and the merged dataset (balanced), by noting metrics namely accuracy, precision, f1 score and recall for each technique. Experimental results have demonstrated that a higher accuracy is obtained for the balanced dataset using LightGBM technique with an accuracy score of 90.6% as compared to 75.9% obtained using Random Forest method on the unbalanced dataset.
Time series regression based on Bayesian model averaging and principal component analysis

This paper proposed an adaptive prediction model for high-dimensional time series data based on model averaging method and principal component analysis.Specifically, this paper considers the case where the response variable is a scalar and the predictor variable is a time series.Firstly, the high-dimensional time series data is extracted information by principal component analysis.Secondly, the Bayesian model averaging method is used to perform the forecast task based on the principal component projection matrix.The proposed method can effectively deal with the unsupervised nature of PCA and avoid the problem of selecting the number of PCA.It is demonstrated that the proposed method is competitive compared with the lasso regression and the ridge regression by real data analyses.
Fraud Detection System Using Facial Recognition Based On Google Teachable Machine for Banking Applications

Security is major concern for any system. The scammer continually tries to obtain the user's account information by applying different tricks to perform fraud that cost the banking system and user huge loss. Machine learning based techniques are most extensively being used to avoid this risk. Face recognition based systems are not sufficient especially in banking sectors. Teachable Machine is a webbased tool that makes building machine learning models fast, easy, and accessible to everyone. So in order to stop those fraudulent we should need powerful fraud detection method or system by which detect fraud .We have proposed a only method by using face recognition features along with face recognition systems to boot the security level of the society against the fraudsters. It is using the features of face recognition and face recognition authentication which makes the transaction more secure as compared to the traditional payment.
Blockchain Based System for the Verification and Safekeeping of Medical Examination Record of Government Employees

Medical data is very sensitive and vital in form of Electronic Health Record (EHR) for healthcare sector, storage of this data is one of an important challenge. Cloud is providing storage services with cost and energy efficiency, data over the cloud is secure but not much over the public cloud. EHR require more security and there is strict compliance of HIPAA and HL7 over the security of patient’s medical record. Sensitive EHR over the public cloud where the urgent verification is required is also great challenge. Different cryptography techniques are proposed to secure the data but there are much chances to compromise the keys. Our proposed technique for secure verification of data store locally with the use of blockchain technology that make information immutable and distributed, so that the medical record could be verify from different offices and authorities using blockchain technology. As a model we have taken an important hospital of the city (working under the Government of the Punjab, Health Department) where all medical examination record (MER) is maintained and Medical Fitness Certificate (MFC) issues to Govt. employee. We shall use Hyperledger Fabric to develop the Private/Consortium network of all Tertiary Care Hospital in the city, headed by Office of Secretary Specialized Healthcare and Medical Education (SSHC&amp;ME). Hash of the medical fitness certificate store in Hyperledger Fabric (HLF) blockchain with report id, timestamp patient’s id, and digital signature of Medical Superintendent (MS). Blockchain digital ledger will distributed between high level authorities (offices of Secretaries’ Health, TCH). Office SSHC&amp;ME work as admin of whole HLF network. For verification, verifier (user/Government department) get the hash of medical fitness report and verify it from HLF blockchain network using HLF SDK
Constructions of mismatched binary periodic complementary pairs

Two mismatched binary sequences are called a mismatched binary periodic complementary pair (MB-PCP) if the sum of their periodic crosscorrelation functions is a delta function.Such a pair is a generalization of the well-known Golay sequence pair, and has applications in the scenario where the mismatched filter is used.Up to now, there are only MB-PCPs of length 3q with peak value 4 or 2q -6, where q ≡ 3 ( mod 4) is a prime.The objective of this paper is to construct four families of MB-PCPs of length pq via the generalized cyclotomy and the Chinese Remainder Theorem, where p and q are two different odd primes.
Using edge service for secure information systems availability

The purpose of this work is to develop a security model for an information system, involving a combination of Secure Access Service Edge. This approach will allow content to be decrypted and sessions are continuously monitored for risks and trust levels.
Recommendation of the Best Trading Partner Region through Supplier-Buyer Matching Using Deep Learning

Currently, the way suppliers and buyers find the best business partners on the supply chain is not far from simply focusing on low costs. This study aims to present a way to explore the optimal business partner to construct the best supply chain in consideration of supply chain risks, hidden costs, and opportunities to create added value. More specifically, the probability that a company finds an best business partner between regions or within regions is calculated and presented. In this study, we present a method of developing an index called a transaction possibility score using an artificial intelligence (deep learning) model to find a business partner as an best supplier.&#x0D; We hope that the results of this study will not only explore the best business partners of each local company, but also help reorganize the local industrial structure in Korea.
Impact of audit efforts of the avoid losses firms on accounting transparency: Focused on audit time and audit fees

This study confirms whether the effect of lowering accounting transparency was mitigated when the audit effort of the avoid losses firms was high. Firms in the avoid losses section may have lower accounting transparency, but the purpose is to confirm that the moderating effect on the auditor's characteristics(audit time, audit fee) can alleviate the effect of lowering accounting transparency. As a result of the analysis, we confirm that accounting transparency was alleviated as the audit effort (audit fee, audit time) of the avoid losses firms increased. This can be interpreted as an increase in audit efforts of the avoid losses firms alleviated the decrease in accounting transparency by complying with accounting and audit standards by fully exercising their monitoring and supervisory functions. In addition, it can be seen that the same result was found when the deficit avoidance section was analyzed as a company from a narrow section to a wide section as part of robustness. This results imply that accounting transparency varies depending on the audit efforts of the avoid losses firms. Through this, it is contributing to the verification that the impact of the avoid losses firms on accounting transparency acts differently depending on audit time and audit fees. These results suggest changes in the audit environment in Korea to improve the accounting transparency of firms in the crisis of deficit.
Comparison of the Structural Relationship between the Factors Influencing the Psychological Well-being of Adult Learners in the University Degree Courses in Korean and Chinese

본 연구는 한국과 중국의 대학 학위과정 성인학습자의 교육서비스 질, 자기주도학습능력, 학습몰입이 심리적 안녕감에 미치는 영향을 구조적 관계에 의하여 분석하고, 교육서비스 질과 심리적 안녕감의 관계에 대한 자기주도학습능력 및 학습몰입의 매개효과를 분석하여 비교하였다. 연구방법은 한국의 LiFE 사업에 참여한 대학의 성인학습자 303명과 중국의 대학평생교육원 학력인정과정 성인학습자 528명을 대상으로 설문을 실시하여 가정의 검토, 구조방정식 추정 및 매개효과를 비교‧분석하였다. 이에 대한 분석결과를 보면, 첫째, 한국 성인학습자의 심리적 안녕감에 영향을 미치는 변인 간의 구조적 관계는 교육서비스 질과 자기주도학습능력, 교육서비스 질과 학습몰입, 자기주도학습능력과 학습몰입, 학습몰입과 심리적 안녕감의 경로가 유의하게 나타난 반면, 교육서비스 질과 심리적 안녕감, 자기주도학습능력과 심리적 안녕감의 경로가 유의하지 않았다. 중국 성인학습자의 심리적 안녕감에 영향을 미치는 변인간의 구조적 관계는 교육서비스 질과 심리적 안녕감, 교육서비스 질과 자기주도학습능력, 교육서비스 질과 학습몰입, 자기주도학습능력과 학습몰입, 학습몰입과 심리적 안녕감의 경로가 유의하게 나타난 반면, 자기주도학습능력과 심리적 안녕감의 경로가 유의하지 않았다. 둘째, 학위과정 성인학습자의 교육서비스 질과 심리적 안녕감의 매개 효과는 학습몰입만이 단일매개하고, 자기주도학습능력과 학습몰입이 이중매개하는 결과로 한국과 중국이 같게 나타났다. 이상과 같이 중국 성인학습자의 교육서비스 질과 심리적 안녕감의 관계에 대하여 중국은 직접적인 관계가 있는 반면에 한국은 관계가 없고, 나머지 변인들은 심리적 안녕감에 한국과 중국이 같이 영향을 미치는 것으로 확인되었다.This study was to compare the structural relationship between educational service quality, self-directed learning ability, learning commitment, and psychological well-being of adult learners in the university degree achievement courses in Korea and China, and compared the mediating effect of self-directed learning ability and learning well-being. The research method conducted a survey of 303 adult learners at universities participating in the LiFE project in Korea and 528 adult learners in the academic qualification course of the University Lifelong Education Center in China to compare and analyze the assumptions, structural equation estimation, and mediating effects. According to the analysis results, first, the structural relationship between variables that affect the psychological well-being of Korean adult learners was significant in education service quality and self-directed learning ability, education service quality and learning commitment, and psychological well-being. In the structural relationship between variables affecting the psychological well-being of Chinese adult learners, the paths of educational service quality and psychological well-being, educational service quality and learning commitment, self-directed learning ability and psychological well-being were significant, but the paths of self-directed learning ability and psychological well-being were not significant. Second, the mediating effect of educational service quality and psychological well-being of adult learners in the degree acquisition process was the same as that of Korea and China as a result of single-mediation of learning commitment and self-directed learning ability and learning commitment. As described above, it was confirmed that China has a direct relationship with the relationship between the educational service quality and psychological well-being of Chinese adult learners, while Korea has no relationship with the rest of the variables have an effect on psychological well-being.