Reviewerly/exHarmony huggingface dataset:
Student Academic Record Management System Framework using Oracle Apex

The Student Academic Record System is an increasingly important aspect in any of the academic Institutions especially in the Technical and Higher Education. The challenges for the administrator and management to make several academic decisions with reference to academic reports are very high. In some cases, the system could not provide ample solution to several problems such as improper course registration, tracking of advisee courses, and inaccurate marks calculation due to manual entry. These things could lead to erroneous reports that could affect the managements decision. In addition, a data or a record that is wrongly entered into a system could affect the result of one or more academic reports that are being required. This paper will provide a structural framework that gives an alternative, complete solution for record keeping and management of useful information for management decision. It will address many issues related to course assessment, course record management that includes record keeping of the student marks, and course tracking. The Smart College Solution-Student Academic Record System (SCS-SARS) will provide a user friendly, reliable and accurate web-based system using Oracle Apex. It will provide an accurate academic statistical and graphical report that can be used by the management in their decision-making
Utilisation of audible steganography to organise and analyse the text within WAV files

This project seeks to encrypt audio cover files and create temporal domain audio steganography, an audio file with hidden options and text. The mean square error (MSE), mean absolute error (MAE), signal-to-noise ratio (SNR), and cross-correlation analysis identify audio stream text data by comparing 8-bit and 16-bit pulse-code modulation (PCM) audio. This study estimates how many characters can be added to an audio file without changing its structure. MP3's bit rate is audible. Audio steganography is a secure, cost-effective approach to encrypting network data. It's useful for steganography due to low noise distortion. Undetectable embedding is preferable. The suggested technique improves accuracy at low embed levels, according to testing. The suggested strategy delivers the highest peak signal-to-noise ratio (PSNR) with hidden information in the first, second, and third least-significant bit (LSBs). Three LSB had 98% accuracy and the lowest false alarm rate (less than 5%). Experiments reveal that this study's method extracts audio. The recommended method is hard and capable. Novel audio file steganography is imperceptible and recovers messages, and text message length affects robustness.
Deceptive web-review detection strategies: a survey

Deceptive reviews on internet platforms are a harsh reality in today's world. Specific services, businesses, and products are praised or vilified in these reviews. In an online or web society, users get help from reviews before making a decision, and in a similar way, web reviews are also very helpful for organisations to keep them updated as per customer needs. A lot of effort has gone into detecting this sort of review. Apart from evaluating state-of-the-art research papers on fraudulent review identification, a taxonomy of machine learning algorithms for detecting deceptive or non-genuine web reviews is provided in this paper. It begins with a broad introduction to sentiment analysis, web reviews, deceptive reviews, and machine learning. Thereafter, providing crucial information regarding available deceptive review detection approaches, along with datasets, methodologies, and their performance. It critically summarises existing techniques to find out research gaps w.r.t. supervised, unsupervised, and semi-supervised methods and also quantitatively with the help of specific datasets such as Yelp and AWS. The paper also looks at research gaps and future recommendations for detecting deceptive reviews.
STEGANOGRAPHY BASED MULTI LEVEL PASSWORD AUTHENTICATION SYSTEM IN BANKING SECTOR

Nowadays password selection is very important verification process. Various password authentication techniques are implemented in many applications. Traditional text-based password schemes are suffered from shoulder surfing problem, easily guessed by others. Password is easily identified by others. Graphical password, steganography-based password authentication technologies creating strong security for user password. The process of information hiding is known as steganography. It is used to shield the user password or hide the credential information. Then, graphical password authentication is authentication which increases the accuracy level. The proposed work described the secureness of web login application. Water marking based graphical password is best for accessing the web accounts. Image level authentication is easily understandable by authorized user. This paper majorly described, what requirement is needed by todays technologies, and focused to minimum password gesture attacks.
Automatic Identification of Video Game Development Problems using Word Embedding and Ensemble Classifiers

The video game development industry, also known as the interactive entertainment business, is involved in building, marketing, advertising, and monetizing video games. Over the last few years, this industry has come into the mainstream from initially being in the focused market. The growing video gamer demographic has increased by video game development methods and techniques. A postmortem of a video game is a close examination of the video game’s development phase and an analysis of what went right or wrong with the video game. Unfortunately, since there is not much understanding regarding the challenges encountered by programmers, there is a lack of trustworthiness primarily because postmortems lack a proper structure and are informally written. In this work, with the help of word embeddings and ensemble machine learning classifiers, a systematic analysis is performed on various technical and non-technical issues faced by the video game industry. It is believed that automation and machine learning classifiers could aid game developers in identifying what problem they are facing, given the quote (description), and thus be able to figure out a solution quickly. Frequently committed mistakes could be identified and avoided, and this work could act as a starting point to further consider software development and video game development in the same context.
Mimic Octopus Attack: Dynamic Camouflage Adversarial Examples Using Mimetic Feature for 3D Humans

Physical adversarial attacks in object detection have become an attractive topic. Many works have proposed adversarial patches or camouflage to perform successful attacks in the real world, but all of these methods have drawbacks, especially for 3D humans. One is that the camouflage-based method is not dynamic or mimetic enough. That is, the adversarial texture is not rendered in conjunction with the background features of the target, which somehow violates the definition of adversarial examples; the other is that there is no detailing of non-rigid physical surfaces, such that the rendered textures are not robust and very rough in 3D scenarios. In this paper, we propose the Mimic Octopus Attack (MOA) to overcome the above gap, a novel method for generating a mimetic and robust physical adversarial texture to target objects to camouflage them against detectors with Multi-View and Multi-Scene. To achieve joint optimization, it utilizes the combined iterative training of mimetic style loss, adversarial loss, and human eye intuition. Experiments in specific scenarios of CARLA, which is generally recognized as an alternative to physical domains, demonstrate its advanced performance, resulting in a 67.62% decrease in mAP@0.5 for the YOLO-V5 detector compared to normal, an average increase of 4.14% compared to the state-of-the-art attacks and an average ASR of up to 85.28%. Besides, the robustness in attacking diverse populations and detectors of MOA proves its outstanding transferability.
A Big Data Analysis on Europe Area Studies using Text Mining, Network, and Topic Modeling

The This study aims to analyze the papers on Europe published in Korea during the past 2000-2020 years. As the research methodologies, this study uses the text mining and Social Network Analysis such as frequency analysis, several centrality analyses, and topic analysis. After analyzing the empirical results, there has been a tendency to change the key words and centrality coefficients between 2000-2010 and 2011-2020 years. While the most frequent keywords were policy, system, culture, globalization, and state during 2000-2010 years, the main keywords were policy, integration, market, system, and labor during 2011-2020 years, The degree and closeness centrality analyses appeared the higher frequency key words. However, the eigenvector centrality appeared very different from the order of frequency key words. The topic analysis shows that the culture, migration, system, security, and democracy were the most important keywords during 2000-2010 years but policy, integration, economy, market, employment, and free trade agreement became the most important keywords duing 2011-2020 years in topic analysis.
Discussions on the issuance of China’s digital RMB and cross-border transactions

After the COVID-19 pandemic, a cashless society is rapidly emerging into reality. With the prolonged COVID-19 in 2020, discussions on the introduction of Central Bank Digital Currency (CBDC) issued by each country’s central banks are growing. In addition, consumers prefer payment methods for mobile first and fully digital. In particular, China is leading the way in issuing digital currency. Since 2014, the People’s Bank of China has established a research organization dedicated to digital currency and has been promoting pilot tests step by step. Starting with the 2022 Beijing Winter Olympics, the scope of pilot tests is being expanded to enable regional transactions in intra-regional transactions with the aim of commercializing digital RMB. In addition, China is conducting pilot tests with the aim of increasing the efficiency of the international payment and settlement system with the digital RMB having an institutional and system advantage in cross-border transactions. The digital renminbi cross-border payment system test is still in its early stages of exploration. In the short term, it will take a lot of time for digital RMB technology to be properly established. However, each country’s digital currency development is also in its early stages, so it is important to make it compatible with other countries’ digital currencies. Korea also needs to take an advantageous position in establishing a global digital currency payment system by leading the establishment of global norms and systems related to CBDC. As such, it is an important task to establish specific international standards through international discussions and cooperation on digital currency that can be traded across borders.
A Study on Online Strategy for Globalization of Education Higher Education Service in Korea

This study aims to derive innovation strategies for the higher education in the post-pandemic era through analyzing the feasibility of the online education as a global higher education service strategy for Korean universities. To this end, the study analysed the awareness of the necessity and contribution of university’s g lobalization a nd s uccess f actors o f global o nline education, s urveying for 2 2 7 faculty members related to international education at 147 universities in Korea. The study results present that the faculty members aware of the necessity and effectiveness of the internationalization and online education strategy for the higher education service industry to secure global competitiveness for Korean universities. Lastly, the study suggested practical implications for the direction of higher education policy for universities and educational authorities.
ランダムパターン投影によるSfM三次元計測の精度向上手法

SfM can reconstruct the 3D shape of an object with high accuracy if there are many feature points on the surface to be measured. However, in the field of architecture, there is a problem that building surfaces are beautifully preserved and the shape cannot be reconstructed because of the small number of feature points. In this study, we proposed a method to improve the measurement accuracy of SfM by projecting a random pattern on a wall surface and distributing a large number of feature points. Experiments confirmed that the measurement accuracy was as good as that of a laser scanner.
Projection Mapping-Based Interactive Gimmick Picture Book with Visual Illusion Effects

In this study, we proposed an electronic gimmick picture book system based on projection mapping for early childhood education in ordinary households. In our system, the visual effects of pop-up gimmicks in books are electronically reproduced by combining projection mapping, three-dimensional expression, and visual illusion effects. In addition, the proposed projector-camera system can provide children effective interaction experiences, which will have positive effects on their early childhood education. The performance of the proposed system was validated by projection experiments.
Monitoring System Using Ceiling Camera and Mobile Robot

In this letter, we propose a monitoring system that employs a ceiling camera and a mobile robot. Ceiling camera is used to estimate the position of a fallen person, which is used by the robot to navigate to the person’s vicinity to examine his or her condition. This is done by transmitting the height information of obstacles and the fallen person from the ceiling camera to the robot so that it can accurately localize itself and plan a safe route. An actual system was constructed and verified that the required movements could be executed.
Concept and Implementation of the Intuitive Controller MISORI-2: Development of a Robot Manual Controller Without Laptop and Game Controller

To participate in the Standard Disaster Robotics Challenge in the Disaster Robotics Category at the World Robot Challenge in the World Robot Summit 2020 Fukushima Competition, we have developed a main-replica type controller using which anyone can easily control the disaster robot. The two student operators finished second in the competition despite having only five hours of practice time. In this study, we present the results of surveying the essential elements of a controller for disaster response robots, and introduce the controller that is designed and implemented based on the survey.
A Systematic Survey of Automatic Text Summarization Using Deep Learning Techniques

Today, with the advancement of web technology and the accessibility of the Internet, one can now share their opinion almost everywhere. The Internet user is now not limited to consume the information; instead, they are now information producer. This evolution has resulted in an enormous quantity of data produced over social media and other platforms. This overloaded information makes it complicated for the user to extract and summarize this content. Thus, text summarization is required to generate a shorter version of information to handle the complexity of data. This need leads to exposure to various summarization techniques. This paper gives an overview to the summarization process and studies recent advancements in deep learning-based summarization. Dataset and summary evaluation methodologies are discussed in the area of text summarization. Finally, we conclude the study with various observations, challenges, and future scope in automatic text summarization. This paper is a contribution in providing help to researchers to better understand summarization and find new opportunities in the field of summarization.
Estimation of Accuracy in Gender Identification Based on Voice Signals Using Different Classifiers

This paper presents the estimation of accuracy of different classifiers which are used for the identification of gender with the help of voice signals. The voice signal has rich information about the speaker. The analysis of the voice signals is very vital for accurate and fast identification of gender. In this paper, the Mel Frequency Cepstral Coefficients (MFCCs) are used as an extracted feature of the voice signals of the speakers. MFCCs are the most convenient and reliable feature which sets to configure the gender identification system. Recurrent Neural Network—Bidirectional Long Short-Term Memory (RNN-BiLSTM), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) Gaussian Mixture Model (GMM) and K-Nearest Neighbor (KNN) are utilized as classifiers in this proposed work. In this article, the RNN-BiLSTM classifier has single-layer architecture, while SVM and LDA have a kfold value of 5. The highest accuracy for gender identification is found as 88.66%. The result of the simulations shows that the accuracy of the RNN is always found at a higher value as compared to SVM, LDA, GMM and KNN.
A Three-Stage Model Fusion Method for Out-of-Distribution Generalization

Training a model from scratch in a data-deficient environment is a challenging task. In this challenge, multiple differentiated backbones are used to train, and a number of tricks are used to assist in model training, such as initializing weights, mixup, and cutmix. Finally, we propose a three-stage model fusion to improve our accuracy. Our final accuracy of Top-1 on the public test set is 84.62421%.
Spatio-Temporal Attention for Cloth-Changing ReID in Videos

In the recent past, the focus of the research community in the field of person re-identification (ReID) has gradually shifted towards video-based ReID where the goal is to identify and associate specific person identities from videos captured by different cameras at different times. A key challenge is to effectively model spatial and temporal information for robust and discrimintative video feature representation. Another challenge arises from the assumption that the clothing of the target persons would remain consistent over long periods of time and thus, most of the existing methods rely on clothing appearance for re-identification. Such assumptions lead to errors in practical scenarios where clothing consistency does not hold true. An additional challenge comes in the form of limitations faced by existing methods that largely employ CNN-based networks since CNNs can only exploit local dependencies and lose significant information due to downsampling operations employed. To overcome all these challenges, we propose a Vision-transformer-based framework exploring space-time self-attention to address the problem of long-term cloth-changing ReID in videos (CCVID-ReID). For more unique discriminative representation, we believe that soft-biometric information such as gait features can be paired with the video features from the transformer-based framework. For getting such rich dynamic information, we use an existing state-of-the-art model for 3D motion estimation, VIBE. To provide compelling evidence in favour of our approach of utilizing spatio-temporal information to address CCVID-ReID, we evaluate our method on a variant of recently published long-term cloth-changing ReID dataset, PRCC. The experiments demonstrate the proposed approach achieves state-of-the-art results which, we believe, will invite further focus in this direction.
Identifying Auxiliary or Adversarial Tasks Using Necessary Condition Analysis for Adversarial Multi-task Video Understanding

There has been an increasing interest in multi-task learning for video understanding in recent years. In this work, we propose a generalized notion of multi-task learning by incorporating both auxiliary tasks that the model should perform well on and adversarial tasks that the model should not perform well on. We employ Necessary Condition Analysis (NCA) as a data-driven approach for deciding what category these tasks should fall in. Our novel proposed framework, Adversarial Multi-Task Neural Networks (AMT), penalizes adversarial tasks, determined by NCA to be scene recognition in the Holistic Video Understanding (HVU) dataset, to improve action recognition. This upends the common assumption that the model should always be encouraged to do well on all tasks in multi-task learning. Simultaneously, AMT still retains all the benefits of multi-task learning as a generalization of existing methods and uses object recognition as an auxiliary task to aid action recognition. We introduce two challenging Scene-Invariant test splits of HVU, where the model is evaluated on action-scene co-occurrences not encountered in training. We show that our approach improves accuracy by $$\sim $$ 3% and encourages the model to attend to action features instead of correlation-biasing scene features.
Interactive Multimodal Robot Dialog Using Pointing Gesture Recognition

Pointing gestures are an intuitive and ubiquitous way of human communication and thus constitute a crucial aspect of human-robot interaction. However, isolated pointing recognition is not sufficient, as humans usually accompany their gestures with relevant natural language commands. As ambiguities can occur both visually and textually, an interactive dialog is required to resolve a user’s intentions. In this work, we tackle this problem and present a system for interactive, multimodal, task-oriented robot dialog using pointing gesture recognition. Specifically, we propose a pipeline constituted of state-of-the-art computer vision components to recognize objects, hands, hand orientation as well as human pose, and combine this information to identify not only pointing gesture presence but also the objects which are pointed at. Furthermore, we provide a natural language understanding module which considers pointing information to distinguish unambiguous from ambiguous commands and responds accordingly. Both components are integrated into the proposed interactive and multimodal dialog system. For evaluation purposes, we introduce a challenging benchmark set for pointing recognition from human demonstration videos in unconstrained real-world scenes. Finally, we present experimental results of both the individual components as well as the overall dialog system.
Detecting API Missing-Check Bugs Through Complete Cross Checking of Erroneous Returns

Missing-check of erroneous execution states may cause critical security problems, such as null pointer dereference bugs or logic errors, which could even crash the systems. It’s still a challenge to decide automatically whether an erroneous state should be validated or not because of the difficulty in understanding API semantics. Cross-checking is a sound method to resolve the problem. However, recent cross-checking studies suffer from poor accuracy due to inaccurate data-flow analysis, leading to the imprecise analysis of many error states and false positives. In this paper, we present ERSAnalyzer (Erroneous Return Status Analyzer), a new static analysis method to improve existing tools to completely detect inter-procedural missing-check bugs of return values in the Linux kernel. At first, our approach identifies the functions which may generate error return status. After that, we propose a new method to find out the pointer parameter variables carrying error semantics except for the return values. Then a complete missing-check analysis on these critical variables is performed to confirm if they are validated before or after functions return. By utilizing cross-checking, ERSAnalyzer achieves higher precision of 71.3% in deciding whether a critical variable is checked. ERSAnalyzer reports 335 cases; 239 of those are potential bugs, 25 are manually proved to be actual missing-check bugs. Limited by the understanding of the code logic and some bugs that have been fixed in the latest version. We finally submitted 12 new bugs to the Linux Kernel, and six of our patches have been accepted up to now. The results show the effectiveness of ERSAnalyzer.