Reviewerly/exHarmony huggingface dataset:
Deep Fake Face Detection Using Deep Learning Tech with LSTM

The fabrication of extremely life like spoof films and pictures that are getting harder to tell apart from actual content is now possible because to the quick advancement of deep fake technology. A number of industries, including cybersecurity, politics, and journalism, are greatly impacted by the widespread use of deepfakes, which seriously jeopardizes the accuracy of digital media. In computer vision, machine learning, and digital forensics, detecting deepfakes has emerged as a crucial topic for study and development. An outline of the most recent cutting-edge methods and difficulties in deep fake detection is given in this abstract. In this article, we go over the fundamental ideas behind deepfake creation and investigate the many approaches used to spot and stop the spread of fake news. Methods include sophisticated machine learning algorithms trained on enormous datasets of real and fake media, as well as conventional forensic investigation. We explore the principal characteristics and artifacts that differentiate authentic video from deepfakes, such as disparities in audio-visual synchronization, aberrant eye movements, and inconsistent facial emotions. Convolutional neural networks (CNNs) and generative adversarial networks (GANs), two deep learning frameworks, have been used by researchers to create sophisticated detection models that can recognize minute modifications in multimedia information. The fast developments in deep fake generating techniques, however, continue to exceed efforts in detection and mitigation, making deep fake detection a daunting problem. The issue is made worse by the democratization of deepfake technology and its accessibility to non-experts, which calls for creative solutions and multidisciplinary cooperation to counter this expanding danger. Keywords: convolutional neural network,, generative adversarial network, deep fake ,long short term memory,
Impact of Project BANAS Materials to the Academic Performance of Learners: Basis for Enhancement Plan

This descriptive quantitative research is a scientific inquiry which aimed to determine the level of Impact of Project BANAS Materials (a collective name of the competency-based division-initiated print and non-print resources in Social Studies) in relation to academic performance of learners. This study was conducted in the seven (7) elementary schools of District VII, Division of Bacolod City during the School Year 2022-2023. Researcher-made questionnaire used in this study was validated by three (3) experts. It has undergone reliability testing with thirty (30) respondents at Andres Bonifacio Elementary School I. Data were collected during the Second Quarter of the School Year from three hundred eighty-three (383) learners in Grades 1 to 5. Highlight of this study examined and analyzed the level of Impact of BANAS materials in terms of these four (4) areas namely: Alignment of Content, Quality of Material, Applicability, and Ease of Use. Findings showed that BANAS Materials in terms of these four (4) areas have a very high impact regardless of sex, grade level, school, and average family monthly income. Based on these findings, BANAS Materials is highly recommended to be utilized by Social Studies teachers and learners as supplementary reading and teaching materials respectively. Results further reveal that there is a need to conduct division training-workshops in the development and quality assurance of additional BANAS Materials focused on competencies not covered in the BANAS Outputs developed in the previous years. Conduct of SOK-SAY-LYMPICS Series: BANAS Quizzes, ATBP is highly recommended as sustainable initiatives aligned on Assessment and Monitoring Key Result Areas. Through Policy Recommendation, the long-term goal of this research is the Institutionalization of BANAS Learning Hub in 46 Elementary Schools in the Division of Bacolod City. Not only regular learners are the target direct beneficiaries of BANAS Print and Non-Print Materials but also Madrasah Education Program (MEP) and Special Needs Education (SNED) learners. Sustainability and successful implementation of this research-cum-innovation needs the collaboration between and among school leaders and DepEd Officials to truly realize the end goals on the need to address scarcity of localized resources, improve reading literacy across disciplines through in-depth love for one’s unique and distinct local heritage. Sequel researches are also highly recommended to done by future teacher-researchers.
Students' Interest in Learning English at Iain Syekh Nurjati Cirebon: Study of Instrumental and Integrative Motivation

Motivation is vital for learning and mastering a language. Motivation related to second language learning is integrative and instrumental motivation. This research aims to describe students’ interest in learning English from the terms instrumental and integrative motivation. The research design is quantitative descriptive. The information obtained is interpreted as tables, diagrams and sentences. The research location is at the IAIN Syekh Nurjati Cirebon campus for the odd academic year 2023-2024. The sample size was 100 students studying English using simple random sampling techniques. Data collection tools are questionnaires related to instrumental and integrative motivation, with 20 statements. The results show that integrative motivation is higher than instrumental motivation for various reasons. The instrumental motivation of students' desire to study English leads to the conclusion that their motivation is tied to their employment, future profession, and education. Regarding integration motivation, they recognized that English is a crucial language for worldwide communication. They are also eager to visit English-speaking countries, engage with native speakers, and utilize English with English-speaking friends or acquaintances. The majority of students learn English because of integrative motivation.
Crossed-IoT Device Portability of Electromagnetic Side Channel Analysis: Challenges and Dataset

Internet of Things (IoT) refers to the network of interconnected physical devices, vehicles, home appliances, and other items embedded with sensors, software, and connectivity, enabling them to collect and exchange data. IoT forensics aim to investigate cybercrimes, security breaches, and other malicious activities that may have taken place on these connected devices. In particular, Electromagnetic Side-Channel Analysis (EM-SCA) has become an essential tool for IoT forensics due to its ability to reveal confidential information about the internal functions of IoT devices without interfering with these devices or wiretapping their networks. However, the accuracy and reliability of EM-SCA results can be limited by device variability, environmental factors, and data collection and processing methods. In fact, very few studies have explored potential solutions to address the significant impact of these limitations on the accuracy of EM-SCA approaches applied to crossed IoT devices. Therefore, this paper examines the impact of device variability on the accuracy and reliability of machine learning (ML)-based approaches for EM-SCA. We first present the background, basic concepts and techniques used to evaluate the limitations of current EM-SCA approaches and datasets. Our study then addresses one important limitation, which is caused by the multi-core architecture of the processors (aka. System-On-Chip). We present an approach to collect the EM-SCA datasets and demonstrate the feasibility of using transfer learning to obtain more meaningful and reliable results from EM-SCA in IoT forensics of crossed-IoT devices. Moreover, this study contributes a new dataset for using deep learning (DL) models in analyzing Electromagnetic Side-Channel data with regard to the cross-device portability matter.
Research on the construction of urban wisdom logistics platform based on blockchain

As a modern industry, smart logistics is of great significance for promoting the transformation and development of the green economy, optimising the government's macro-control, and creating an upgraded version of China's economy. In order to improve the efficiency of logistics, improve the low information transparency and poor security of urban logistics industry, according to the current situation of the logistics industry, based on the technical characteristics of the blockchain, the model of the smart logistics platform is constructed, and then the smart logistics platform is designed from the technical architecture of the smart logistics platform, the security layer, the business application layer, the data layer, and the data layer, and finally, this paper clarifies the aspects of reducing the cost of logistics operation, improving the transparency and traceability of data information, strengthening the information security, and optimising the supply chain management. Finally, this paper clarifies that blockchain has a significant impact on the development of smart logistics from the aspects of reducing logistics operation cost, improving data information transparency and traceability, strengthening information security, and optimising supply chain management.
Roadside LiDAR background filtering method based on differences in the distance of points in the spherical coordinates

To obtain object micro traffic information, LiDAR plays an important role in intelligent perception. Many background filtering methods based on LiDAR can detect the target, but the data process is not simple enough and data information will be lost easily. In this work, from the principle of original point cloud data capture, an advanced background filtering algorithm based on PCAP (Packet Capture) file is proposed. Firstly, a data extraction algorithm for PCAP files is designed. Then, one non-target frame is chosen as the background frame. The distance difference method is used to filter the background under the spherical coordinate system with the relative horizontal angle and vertical angle as discrete coordinates. The algorithm is improved twice by reconstructing the background frame based on the hierarchical clustering method and changing the distance difference threshold. Finally, the effectiveness of the three algorithms is verified.
Modeling analysis of flight support vehicle scheduling based on fuzzy colored petri nets

In order to better describe the flight support vehicle scheduling system, a fuzzy colored Petri net (FCPN) model is proposed, and its formal definition and specific inference algorithm are listed. A dispatching model with active strategies is developed using FCPN. An application example yields that the FCPN model significantly reduces the model complexity and is able to more accurately describe the vehicle scheduling system.
Two step vehicle trajectory reconstruction strategy based on an improved outlier detection and data smoothing algorithm

Vehicle trajectory data contains abundant traffic information and plays a key role in traffic flow research. Due to unsuitable sensor position or perspective, there are some local errors in existing vehicle trajectory data sets. To solve this problem, a two-step vehicle trajectory data reconstruction strategy is proposed in this paper. In the first step, an outlier detection algorithm combines the cluster-based local outlier factor (CBLOF) and the k-means clustering algorithm (KCBLOF) is proposed to identify outlier in object dataset. In the second step, Savitzky-Golay (SG) filter is selected to smooth the data flow containing noise. In this paper, the prototype data of I-80 highway in the open source trajectory data set NGSIM is used for case study. Taking vehicle 1 as an example, the speed and acceleration data of vehicle 1 are reconstructed. Time Windows with length of 11 and 21 are selected respectively to implement SG filter. The results show that when the time window value is 21, the trajectory data shows a better smoothed result, and the data noise can be obviously eliminated, which verifies that the trajectory data smoothing strategy proposed in this paper has a certain reliability.
Fake Instagram Account Detection using ML Algorithms

Abstract—Nowadays, the majority of people utilise social networking sites on a daily basis. Numerous people create profiles on social networking websites every day and connect with others there, regardless of their location or time. False identities are used in additional malicious operations in addition to playing a significant part in advanced persistent threats. Users of social networking sites can benefit from them, but they also have to worry about the security of their personal information. We must first determine the user's social network accounts before we can determine who is endorsing threats in these platforms. Social media usage has dramatically increased in recent years, according to statistics. Social networking sites have made things easier as they allow us to connect to people effortlessly and converse with them without the requirement for physical meetups. One of the major issues with Online Social Networks (OSNs) is fake interaction, which is used to artificially boost an account's popularity. Because phoney involvement causes businesses to lose money, inaccurate audience targeting in advertising, inaccurate product prediction systems, and an undesirable social network atmosphere, its detection is essential. Based on the classification, it is required to distinguish between real and phoney profiles on social media.Several categorization techniques have traditionally been used to identify phoney social media accounts. However, there are ways to improve social media's ability to detect phoney profiles. The suggested effort uses technology and machine learning to boost the percentage of predicted phoney profiles. Chi-square technique is used in feature selection models to find the best data. The several machine learning methods, including the Logistic Regression and Random Forest algorithms, are used in the classification approach. The classification outcome based on recall, sensitivity, specificity, f1-score, accuracy, and precision. Keywords—Machine learning, online social networks, Instagram, Social media, Natural language process.
Overview of the socio-economic profile of and challenges faced by African countries

Africa consists of 54 countries, each of which is at a different level of development, and faces a number of challenges. The chapter gives an overview of the level of socio-economic development of African countries, some of the challenges they face, and the roles innovation and IP play in addressing development-related problems.
Human Curriculum Effects Emerge with In-Context Learning in Neural Networks.

Human learning is sensitive to rule-like structure and the curriculum of examples used for training. In tasks governed by succinct rules, learning is more robust when related examples are blocked across trials, but in the absence of such rules, interleaving is more effective. To date, no neural model has simultaneously captured these seemingly contradictory effects. Here we show that this same tradeoff spontaneously emerges with "in-context learning" (ICL) both in neural networks trained with metalearning and in large language models (LLMs). ICL is the ability to learn new tasks "in context" - without weight changes - via an inner-loop algorithm implemented in activation dynamics. Experiments with pretrained LLMs and metalearning transformers show that ICL exhibits the blocking advantage demonstrated in humans on a task involving rule-like structure, and conversely, that concurrent in-weight learning reproduces the interleaving advantage observed in humans on tasks lacking such structure.
An In-Depth Analysis on Efficiency and Vulnerabilities on a Cloud-Based Searchable Symmetric Encryption Solution

Searchable Symmetric Encryption (SSE) has come to be as an integral cryptographic approach in a world where digital privacy is essential. The capacity to search through encrypted data whilst maintaining its integrity meets the most important demand for security and confidentiality in a society that is increasingly dependent on cloud-based services and data storage. SSE offers efficient processing of queries over encrypted datasets, allowing entities to comply with data privacy rules while preserving database usability. Our research goes into this need, concentrating on the development and thorough testing of an SSE system based on Curtmola’s architecture and employing Advanced Encryption Standard (AES) in Cypher Block Chaining (CBC) mode. A primary goal of the research is to conduct a thorough evaluation of the security and performance of the system. In order to assess search performance, a variety of database settings were extensively tested, and the system's security was tested by simulating intricate threat scenarios such as count attacks and leakage abuse. The efficiency of operation and cryptographic robustness of the SSE system are critically examined by these reviews.
Calibration-Free Mobile Eye-Tracking Using Corneal Imaging

In this paper, we present and evaluate a calibration-free mobile eye-traking system. The system’s mobile device consists of three cameras: an IR eye camera, an RGB eye camera, and a front-scene RGB camera. The three cameras build a reliable corneal imaging system that is used to estimate the user’s point of gaze continuously and reliably. The system auto-calibrates the device unobtrusively. Since the user is not required to follow any special instructions to calibrate the system, they can simply put on the eye tracker and start moving around using it. Deep learning algorithms together with 3D geometric computations were used to auto-calibrate the system per user. Once the model is built, a point-to-point transformation from the eye camera to the front camera is computed automatically by matching corneal and scene images, which allows the gaze point in the scene image to be estimated. The system was evaluated by users in real-life scenarios, indoors and outdoors. The average gaze error was 1.6∘ indoors and 1.69∘ outdoors, which is considered very good compared to state-of-the-art approaches.
Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data

Standard machine learning is unable to accommodate inputs which do not belong to the training distribution. The resulting models often give rise to confident incorrect predictions which may lead to devastating consequences. This problem is especially demanding in the context of dense prediction since input images may be only partially anomalous. Previous work has addressed dense out-of-distribution detection by discriminative training with respect to off-the-shelf negative datasets. However, real negative data may lead to over-optimistic evaluation due to possible overlap with test anomalies. To this end, we extend this approach by generating synthetic negative patches along the border of the inlier manifold. We leverage a jointly trained normalizing flow due to a coverage-oriented learning objective and the capability to generate samples at different resolutions. We detect anomalies according to a principled information-theoretic criterion which can be consistently applied through training and inference. The resulting models set the new state of the art on benchmarks for out-of-distribution detection in road-driving scenes and remote sensing imagery despite minimal computational overhead.
Service robots and their implications for service delivery

The digital service revolution will significantly change the way we do business. Service robots in various forms and shapes are a big part of this revolution. In this chapter, we illustrate the implication of service robots for the service sector. We compare service robots with traditional self-service technologies (SSTs) and human service personnel and identify opportunities and challenges. In the Service Robot Deployment Model (SRD Model), we highlight promising areas for human-robot collaboration and discuss the challenges of service robot deployment. Finally, we derive managerial implications for the service frontline in this new reality with service robots.
A Generative Approach to Person Reidentification

Person Re-identification is the task of recognizing comparable subjects across a network of nonoverlapping cameras. This is typically achieved by extracting from the source image a vector of characteristic features of the specific person captured by the camera. Learning a good set of robust, invariant and discriminative features is a complex task, often leveraging contrastive learning. In this article, we explore a different approach, learning the representation of an individual as the conditioning information required to generate images of the specific person starting from random noise. In this way we decouple the identity of the individual from any other information relative to a specific instance (pose, background, etc.), allowing interesting transformations from one identity to another. As generative models, we use the recent diffusion models that have already proven their sensibility to conditioning in many different contexts. The results presented in this article serve as a proof-of-concept. While our current performance on common benchmarks is lower than state-of-the-art techniques, the approach is intriguing and rich of innovative insights, suggesting a wide range of potential improvements along various lines of investigation.
Keyboard Data Protection Technique Using GAN in Password-Based User Authentication: Based on C/D Bit Vulnerability

In computer systems, user authentication technology is required to identify users who use computers. In modern times, various user authentication technologies, including strong security features based on ownership, such as certificates and security cards, have been introduced. Nevertheless, password-based authentication technology is currently mainly used due to its convenience of use and ease of implementation. However, according to Verizon’s “2022 Data Breach Investigations Report”, among all security incidents, security incidents caused by password exposures accounted for 82%. Hence, the security of password authentication technology is important. Consequently, this article analyzes prior research on keyboard data attacks and defense techniques to draw the fundamental reasons for keyboard data attacks and derive countermeasures. The first prior research is about stealing keyboard data, an attack that uses machine learning to steal keyboard data to overcome the limitations of a C/D bit attack. The second prior research is an attack technique that steals keyboard data more efficiently by expanding the features of machine learning used in the first prior research. In this article, based on previous research findings, we proposed a keyboard data protection technique using GAN, a Generative Adversarial Network, and verified its feasibility. To summarize the results of performance evaluation with previous research, the machine learning-based keyboard data attack based on the prior research exhibited a 96.7% attack success rate, while the study’s proposed method significantly decreased the attack success rate by approximately 13%. Notably, in all experiments, the average decrease in the keyboard data classification performance ranged from a minimum of −29% to a maximum of 52%. When evaluating performance based on maximum performance, all performance indicators were found to decrease by more than 50%.