Reviewerly/exHarmony huggingface dataset:
Few-Shot Training GAN for Face Forgery Classification and Segmentation Based on the Fine-Tune Approach

There are many techniques for faking videos that can alter the face in a video to look like another person. This type of fake video has caused a number of information security crises. Many deep learning-based detection methods have been developed for these forgery methods. These detection methods require a large amount of training data and thus cannot develop detectors quickly when new forgery methods emerge. In addition, traditional forgery detection refers to a classifier that outputs real or fake versions of the input images. If the detector can output a prediction of the fake area, i.e., a segmentation version of forgery detection, it will be a great help for forensic work. Thus, in this paper, we propose a GAN-based deep learning approach that allows detection of forged regions using a smaller number of training samples. The generator part of the proposed architecture is used to synthesize predicted segmentation which indicates the fakeness of each pixel. To solve the classification problem, a threshold on the percentage of fake pixels is used to decide whether the input image is fake. For detecting fake videos, frames of the video are extracted and it is detected whether they are fake. If the percentage of fake frames is higher than a given threshold, the video is classified as fake. Compared with other papers, the experimental results show that our method has better classification and segmentation.
Dual-View Single-Shot Multibox Detector at Urban Intersections: Settings and Performance Evaluation

The explosion of artificial intelligence methods has paved the way for more sophisticated smart mobility solutions. In this work, we present a multi-camera video content analysis (VCA) system that exploits a single-shot multibox detector (SSD) network to detect vehicles, riders, and pedestrians and triggers alerts to drivers of public transportation vehicles approaching the surveilled area. The evaluation of the VCA system will address both detection and alert generation performance by combining visual and quantitative approaches. Starting from a SSD model trained for a single camera, we added a second one, under a different field of view (FOV) to improve the accuracy and reliability of the system. Due to real-time constraints, the complexity of the VCA system must be limited, thus calling for a simple multi-view fusion method. According to the experimental test-bed, the use of two cameras achieves a better balance between precision (68%) and recall (84%) with respect to the use of a single camera (i.e., 62% precision and 86% recall). In addition, a system evaluation in temporal terms is provided, showing that missed alerts (false negatives) and wrong alerts (false positives) are typically transitory events. Therefore, adding spatial and temporal redundancy increases the overall reliability of the VCA system.
An IND-CPA Analysis of a Cryptosystem Based on Bivariate Polynomial Reconstruction Problem

The Polynomial Reconstruction Problem (PRP) was introduced in 1999 as a new hard problem in post-quantum cryptography. Augot and Finiasz were the first to design a cryptographic system based on a univariate PRP, which was published at Eurocrypt 2003 and was broken in 2004. In 2013, a bivariate PRP was proposed. The design is a modified version of Augot and Finiasz’s design. Our strategic method, comprising the modified Berlekamp–Welch algorithm and Coron strategies, allowed us to obtain certain secret parameters of the bivariate PRP. This finding resulted in us concluding that the bivariate PRP is not secure against Indistinguishable Chosen-Plaintext Attack (IND-CPA).
Performance improvement factors in quantum radar/illumination

Abstract In this study, we exploit quantum information processing, the research field focusing on quantum two-mode squeezed (QTMS) radar and quantum illumination (QI), to investigate the qualitative behaviors of entanglement, the entropy of formation, and squeezing in these protocols. We use logarithmic negativity to investigate entanglement between the signal and idler and propose strategies to maintain entanglement at room temperature in both protocols. We also calculate the entanglement, squeezing, and entropy for the QTMS radar when the target is present and the signal is transmitted to the target. In addition, by controlling the squeezing parameter which is a tool to control entanglement, entropy, and squeezing, the performance of the QTMS radar can be improved, so this work shows how it is implemented in practice. In both protocols, entanglement is maintained by considering conditions. Since the squeezing parameter controls both signal and idler power and the correlation between them, therefore, the qualitative behavior of squeezing in the QTMS radar and QI is also studied in this research. The significant result obtained from the QI is that the entanglement maintains at high power, low temperature, and high correlation between signal and idler. In contrast, in the QTMS, the entanglement survives when the correlation and power are low, even at room temperature.
A Moth–Flame Optimized Echo State Network and Triplet Feature Extractor for Epilepsy Electro-Encephalography Signals

The analysis of epilepsy electro-encephalography (EEG) signals is of great significance for the diagnosis of epilepsy, which is one of the common neurological diseases of all age groups. With the developments of machine learning, many data-driven models have achieved great performance in EEG signals classification. However, it is difficult to select appropriate hyperparameters for the models to file a specific task. In this paper, an evolutionary algorithm enhanced model is proposed, which optimizes the fixed weights of the reservoir layer of the echo state network (ESN) according to the specific task. As evaluating a feature extractor relies heavily on the classifiers, a new feature distribution evaluation function (FDEF) using the label information of EEG signals is defined as the fitness function, which is an objective way to evaluate the performance of a feature extractor that not only focuses on the degree of dispersion, but also considers the relation amongst triplets. The performance of the proposed method is verified on the Bonn University dataset with an accuracy of 98.16% and on the CHB-MIT dataset with the highest sensitivity of 96.14%. The proposed method outperforms the previous EEG methods, as it can automatically optimize the hyperparameters of ESN to adjust the structure and initial parameters for a specific classification task. Furthermore, the optimization direction by using FDEF as the fitness of MFO no longer relies on the performance of the classifier but on the relative separability amongst classes.
Robust reversible image watermarking scheme based on spread spectrum

Robust reversible watermarking can provide robustness against various attacks besides the ability to recover the cover image. However, robustness and reversibility are somewhat separate in many schemes. The original cover image cannot be recovered even if the watermarked image suffers from a tiny distortion. This paper presents a new robust reversible watermarking scheme by exploring the reversibility of spread-spectrum codes. Watermark bits are embedded by a suggested adaptive spread-spectrum code. The embedding amplitude used in the algorithms is determined by quantizing the source interference of the cover. The proposed scheme is robust to various attacks. Furthermore, since the embedding amplitude is available at the receiver, the original image can be recovered losslessly when there is no attack. Even in the presence of attacks, the original cover images can still be partially recovered. Experimental results demonstrate that the proposed scheme performs well on robustness and watermarked image quality, and provide extra reversibility that resists image distortions.
Machine Learning based Thermal Prediction for Energy-efficient Cloud Computing

Energy-efficient workload management has been widely adopted by data centers for cloud computing. Thermal and energy modeling plays an important role in making decisions on workload management. Machine learning technology has become increasingly popular used in thermal modeling. In this paper, we studied existing machine learning algorithms and methods for thermal prediction for data centers and conducted experiments to investigate the impact of activities on the temperature and energy consumption with CPU-intensive workload. We collected the CPU utilization, temperature, and energy data, and applied several regression models and the XG Boost machine learning model to predict the temperature of the CPU. Performance of the regression models was compared with the XGBoost machine learning models. With more experiments are been conducting to investigate the CPU temperature under various combinations of CPU core utilizations, we will further improve the performance of the XGBoost machine learning model.
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework

This paper presents LE3D; a novel data drift detection framework for preserving data integrity and confidentiality. LE3D is a generalisable platform for evaluating novel drift detection mechanisms within the Internet of Things (IoT) sensor deployments. Our framework operates in a distributed manner, preserving data privacy while still being adaptable to new sensors with minimal online reconfiguration. Our framework currently supports multiple drift estimators for time-series IoT data and can easily be extended to accommodate new data types and drift detection mechanisms. This demo will illustrate the functionality of LE3D under a real-world-like scenario.
Compressed Client Selection for Efficient Communication in Federated Learning

Federated learning (FL) is a distributed approach that enables collaborative training of a shared machine learning (ML) model for a given task. FL requires bandwidth-demanding communication between devices and a central server, which is a cause of many issues such as communication bottlenecks and scaling in the network. Therefore, we introduce the CCS (Compressed Client Selection) algorithm aimed at decreasing the overall communication costs for fitting a model in the FL environment. CCS employs a biased client selection strategy that reduces the number of devices training the ML model and the number of rounds required to reach convergence. In addition, the compression method Count Sketch is implemented to reduce the overhead in client-to-server communication. A use case on the Human Activity Recognition dataset is performed to evaluate CCS and compare it with other state-of-the-art approaches. Experimental evaluations show that CCS efficiently reduces the overall communication overhead for fitting a model and its convergence in a FL environment. In particular, CCS reduces up to 90% the communication overhead compared to literature approaches while providing good convergence even in scenarios where the data are not-independently and identically distributed among client devices.
5G RRC Protocol and Stack Vulnerabilities Detection via Listen-and-Learn

The paper proposes a protocol-independent Listen-and -Learn (LAL) based fuzzing system, which provides a systematic solution for vulnerabilities and unintended emergent behavior detection with sufficient automation and scalability, for 5G and nextG protocols and large-scale open programmable stacks. We use the relay model as our base and capture and interpret packets without prior knowledge of protocols imple-mentation. Radio Resource Control (RRC) is selected proof of concept of the proposed system. Our fuzzing architecture incorporates two abstractions of different dimension fuzzing-command-level and bit-level, and the proposed LAL fuzzing framework focuses on command-level fuzzing covering potential attacks by autonomously generating a comprehensive fuzzing case set. Our analysis of 39 RRC states successfully illustrates 129 vulnerabilities resulting in RRC connection establishment failure from 205 command-level fuzzing cases and reveals insights into exploitable vulnerabilities in each channel of RRC procedure. Furthermore, to assess risks and prevent potential vulnerability, we use the Long Short-Term Memory (LSTM) based model to perform a deep analysis of transaction states in sequenced commands. With the LSTM based model, we efficiently predict more than 95% connection failure at an average duration of 0.059 seconds after the fuzzing attack and provide sufficient time for proactive defense before RRC connection completion or failure, with an average of 3.49 seconds. The rapid vulnerability prediction capability also enables proactive defenses to potential attacks. The proposed fuzzing system offers sufficient automation, scalability, and usability to improve 5G security assurance, and could be used for existing and newly released protocols and stacks validation and real-time system vulnerability detection and prediction.
Anomaly Detection in Surveillance Videos Using Deep Learning

One of the biggest studies on public safety and tracking that has sparked a lot of interest in recent years is deep learning approach. Current public safety methods are existent for counting and detecting persons. But many issues such as aberrant occurring in public spaces are seldom detected and reported to raise an automated alarm. Our proposed method detects anomalies (deviation from normal events) from the video surveillance footages using deep learning and raises an alarm, if anomaly is found. The proposed model is trained to detect anomalies and then it is applied to the video recording of the surveillance that is used to monitor public safety. Then the video is assessed frame by frame to detect anomaly and then if there is match, an alarm is raised.
An Efficient Energy-Aware Load Balancing Method for Cloud Computing

Energy usage has become one of the significant concerns to help accelerate the development of the cloud computing environment because it increases the price of power exponentially to service suppliers and plays an essential role in carbon emissions and thus environmental degradation, and it harms system availability and reliability. Load balancing methods have raised concerns that efficient energy consumption and optimum resource utilization should be considered further. Towards heterogeneous ecosystems, this is an important area of concern. This research presents an efficient energy-aware load balancing (EEALB) method to improve resource utilization and total run time and reduce energy consumption. The proposed method mainly focuses on time and energy awareness. It utilizes the ant colony method for resource allocation. It reduces energy utilization and make span per the essential parameter defined by the cloud service user or provider. The experimental results demonstrate the quality of the proposed method over existing methods.
Sentiment Analysis Using Transfer Learning

Over the recent years, it has been observed that the users like to express their idea, overview or experience about any product, or anything new they have experienced. Thus, this work is showing a greater increase in reviews over the applications which has the review feature for any item; such as any shopping app, example of these can be Amazon or Flipkart, or food delivery applications such as Zomato or Swiggy. The further trend being observed is that these kinds of reviews attract new users, or new sellers or maybe new companies to invest in those kinds of products that has received a good appreciation from a large number of customers. This similar trend has also been observed in different movie titles where people recommend or refrain others from watching a movie. Mostly these reviews are in text format and for companies who have received the review need a proper processing to obtain the meaningful information from them. Suppose, a product receives over a thousand reviews, then it would be tedious task for the company to go through each and every review to know the customer’s thoughts.
A Survey on ASR Systems for Dysarthric Speech

Recently Automatic Speech Recognition (ASR) has been widely overblown with many applications and assistance but orally challenged people, such as people with disordered speech, can’t get much benefits. Speech technologies are very useful on a daily basis to assist people with speech disorders. Dysarthria is a neurological speech disorder caused by significant injury in the left hemisphere of the brain. Dysarthric people have difficulty in the movement of speech-related muscles. As a result of strain on their speech muscles, individuals with dysarthria are able to generate limited speech data for analysis.In order to recognize speech of dysarthria sufferers, a robust technique is needed that can cope with extreme irregularity and narrow training data. This survey details a brief understanding of dysarthric speech characteristics and behavior. It also presents several attempts that have been made to make robust ASR systems for dysarthric speech.
QR Code Detector and Follower with Kalman Filter

For any robot, animal, or social animal to learn, understand, and respond appropriately, visual perception is the most critical capacity. This paper presents an example of computer vision-based research written in the Python programming language which employs libraries like OpenCV and NumPy. To navigate a robot on its own USB 2.0 high-definition camera mounted on robot captures the video stream in the operating area. Identification and decoding of QR code from the visible environment of camera using the image processing and QR code detection algorithm. Tracking of QR code is done using the Kalman filter. The robot will function according to the decision taken by program logic developed in minicomputer depending on data input from the camera.
Privacy-Preserving Social Media with a Disclosure

Social Media are an important communication tool in today's society. In recent years, many events have been held online due to COVID-19, making Social Media an even more important communication tool. However, it is difficult to explicitly imagine the recipients of messages when posting on Social Media and there is a tendency to provide information easily, leading to the existence of inappropriate postings that the user does not intend. Furthermore, it is difficult to disclose information for anonymous posting on Twitter. This cause the link problem between the posts. In our proposal, we realize a way to solve these problems by realizing a Social Media that allows both unlinkable posting and disclose posting. Specifically, unlinkable posts can be changed to named posts, and when the name is changed, it is guaranteed that the person who posted the anonymous post was really the anonymous writer and that the anonymous writer cannot be identified from the anonymous post. We introduced randomized pseudonyms to prevent the viewer from checking a post text based only on the posting name without checking the contents of the posting. We also show how to prevent the attack on our proposed scheme by using hiding property and binding property of the commitment scheme. In addition, we implement the proposed scheme and describe the changes between our proposed scheme and regular post in posting time, publication time, and verification time.
A Comprehensive Study of Classification of Phishing Attacks with its AI/I Detection

Phishing is a cybercrime in which an attacker sends a phony (“ spoof’) communication in the hopes of duping a human victim into divulging important information. These attack entails distributing fake websites links and messages to users and then manipulating and acquiring the information (including login credentials, credit card numbers and unique personal identification) solely for malicious intent. There is a need for an hour to understand phishing attacks as these attacks are evolving and also as the attacks are becoming more intricate and rampant, this results in more people falling prey to such scams. The study seeks to provide greater insight into the types of phishing attacks and their identification, detection, and the provision of existing AI/I solutions to defend the organization against the said malicious attacks. The implications are condemning and to prevent such instances a smarter detection system manned by artificial intelligence and machine learning is used. This research paper aims to start the discussion about phishing attacks by understanding the different type of vectors and methods through which these assaults is carried out. Also, pointing out the necessity of the implementation of intelligence solutions in the organization's cybersecurity infrastructure.
Topic-Based Clustering of Japanese Sentences Using Sentence-BERT

In recent years, text analysis and generation techniques using machine learning have substantially developed and are used to analyze opinions on social networking services and reviews on the Web. However, to build a supervised learning model with BERT to classify sentences based on topics, it is necessary to select topic classes in the field of application and create teacher-labeled datasets corresponding to each class. In this study, we developed a method for clustering Japanese sentences based on topics using a distributed representation obtained using Japanese Sentence-BERT (JSBERT) fine-tuned by the Japanese translation of the Stanford Natural Language Inference corpus. In particular, the use of a distributed representation generated by JSBERT only from the nouns that make up a sentence is an effective way to cluster Japanese sentence datasets based on topics using cosine similarity. We also devised a method to assign an appropriate cluster label to each cluster to make it easier to understand the contents of the cluster. Furthermore, we devised a function to explain why each sentence was classified into the corresponding cluster.
Secure and Efficient Data Integrity Verification Scheme for Cloud Data Storage

Cloud computing is a computing facility which allows its clients to outsource their information on distant cloud based servers without having the pressure of maintaining it. Of several remote data storage issues, data integrity preservation problem has gained much attention of researchers all over the world. Numerous research work has been done by researchers all over the world in the field of data integrity audit in cloud computing. Proposed work is an efficient and stable data integrity verification scheme based on Schnorr signatures. In Schnorr signatures, the signature verification equation is linear and also batch verification of several blocks is possible. Most existing schemes are based on Boneh Lynn and Shacham (BLS) and RSA signature schemes. However, in contrast with existing schemes, the proposed scheme is highly efficient and safe and pays lower verification computation costs proven experimentally in this paper.
Chatbots in Healthcare: Challenges, Technologies and Applications

Artificial intelligence (AI) technologies have been around for more than fifty years. However, current improvements in processing power, the accessibility of huge amounts of data, and improved algorithms have led to significant advancements in AI. A chatbot is a software program with AI that simulates user interactions. Healthcare chatbots gradually eliminate hospital wait times, appointments, and consultation meetups, thus instantly assisting patients in connecting with the right doctor. Chatbots reduce the workload of healthcare providers by decreasing the number of hospital visits and unnecessary treatments, providing suggestions and alerts. However, the introduction of such chatbots in the healthcare domain exposes users to a plethora of challenges. This paper presents a systematic survey of recent developments by researchers in the field of healthcare chatbots. This research brings to light general information regarding the application type, various technologies and evaluation methods that have been used to evaluate the effectiveness of healthcare chatbots and aims to serve as a research guideline which may be valuable for the development of chatbots in various fields.