Reviewerly/exHarmony huggingface dataset:
SCIFI: 3D face reconstruction via smartphone screen lighting

3D face reconstruction based on smartphones has a wide range of applications, such as face recognition, liveness detection, face animation, etc . To realize this function, these smartphones have been deployed with specialized hardware modules (infrared dot projector or depth sensor). However, it will inevitably increase the production cost of smartphones and the operation difficulty of users. In this article, we propose a smartphone screen illumination-based face reconstructIon (SCIFI) framework, which only relies on the front camera and screen lighting. Specifically, we have investigated the calibrated planar lighting to achieve the fine-grained textures in Lambertian-based reconstruction. Further, we introduce the face landmark to align multiple photographs, which aims to adjust the position mismatch caused by hand jitter. Moreover, we propose two different methods to eliminate outlier normals based on the characteristics of human face. Extensive experiments based on different environments (dark and bright), different lighting pattern (4-zones and 9-zones), and different testing subjects have validated the effectiveness and robustness of SCIFI in reconstructing the 3D face surface with a favorable surface shape as well as micro facial texture.
IBM unveils 127-qubit computer

IBM has announced its new 127-quantum bit (qubit) “Eagle” processor.
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We derive a simple proof, based on information theoretic inequalities, of an upper bound on the largest rates of q-ary 2‾-separable codes that improves recent results of Wang for any q≥13. For the case q=2, we recover a result of Lindström, but with a much simpler derivation. The method easily extends to give bounds on B2 codes which, although not improving on Wang's results, use much simpler tools and might be useful for future applications.
Eight Prime Numbers of Modified RSA Algorithm Method for More Secure Single Board Computer Implementation

RSA is the most popular public-key cryptography. The main strength of the algorithm is based on the difficulty of factoring in a large integer number. RSA has also been applied in a system with limited resource environments like single-board computers (SBC). To ensure data security, a recommendation to use a key size longer than 2048 bits generates challenges for implementing RSA in the SBC. This research proposes an EPNR (Eight Prime Numbers of Modified RSA) method, a modified double RSA based on eight prime numbers combined with the CRT method, to speed up the random key generation and decryption mechanism. The method is implemented in a Raspberry Pi 4 Model B+. The running time and security performances of the EPNR were analyzed and compared to the other models. Compared to the others model based on the standard RSA scheme, the proposed model is faster 21.78 times in a random key generation, 9.03 times in encryption and decryption processing. The EPNR has resistance to Wiener, statistical, and factorization attacks (GNFS and Fermat). Using standard RSA in the second encryption mechanism, the GNFS is not yet effective for attacking the proposed model. The modified Fermat Factorization algorithm is more difficult and needed more extra times for factoring a large composite number into eight prime numbers correctly. The method will be useful for implementing certificates authentication and distribution of the secret key. It is very suitable to enhance more secure RSA implementation in an SBC environment.
Integrating Web Application Security Penetration Testing into the Software Development Life Cycle: A Systematic Literature Review

The rising of technology in the 21st century grows in a way that we never thought of before. The COVID-19 pandemic shifted many organizations in different sizes, specialties, and sectors to work online, resulting in a critical need to secure their software, applications, and web-based applications. Furthermore, Organizations that do not implement security in the Software Development Life Cycle (SDLC) process often discover that their applications suffer from security flaws and security bugs, thus exposing their applications to attacks. Applying security measures to the SDLC will ensure that applications are secure from the early planning stages until release. Penetration testing is an essential security measure and is counted as one of the best security practices applied to applications because it focuses on the technical part of application security. Each kind of application the organization uses, in-house developed applications or on-the-shelf applications, has its method for penetration testing. This paper is a systematic literature review that examines the web application security measures and the penetration testing methods and tools to detect vulnerabilities on web applications. Moreover, it gives a general view of adapting penetration testing as a security approach to the SDLC.
Speaker Recognition System with Limited Data based on LightGBM and Fusion Features

The field of speaker recognition has recently developed rapidly, while the accuracy of speaker recognition has generally been declining due to the limited data. Considering the inadequacy of the single feature parameter, the paper selects five common features and gets a new feature M-IM-G-L-BFCC by fusing them. LightGBM, an advanced ensemble learning algorithm in machine learning, is then selected for pattern recognition and a large number of experiments are carried out on different datasets. The last simulation results show that the new fused feature parameters can represent more features, which has greatly improved the recognition accuracy of LightGBM.
A comparative study of GPU metaheuristics for data clustering

In this work, we conduct a comparative study of GPU accelerated metaheuristics for data clustering. Three population-based metaheuristics were implemented in GPU: Particle Swarm Optimization (PSO), Differential Evolution (DE), Scatter Search (SS). These metaheuristics were compared with the state-of-the-art methods for data clustering considering both runtime efficiency and solution quality. GPU-PSO and GPU-DE algorithms demonstrated competitive performance in the data sets proposed by the literature, as well as real-world problems. Moreover, experimental results show that our GPU proposal obtained an average speedup of 175x over the CPU-only implementation.
Identification Method for Endpoint Devices on Low-Power Wide-Area Networks Using Digital Object Architecture with Blockchain Technology Integration

The paper explores the possibility of interaction between the architecture of digital objects, as an identification system for endpoint devices on Low-Power Wide-Area Networks, and blockchain technology, as a particular case of using distributed ledger technologies. The work covered the organization of the blockchain network, the architecture of the model stand developed, as well as a number of studies reflecting the results of the interaction of the identification system with the blockchain network in terms of the time of creation of an entity in LHS and the record thereof in the blockchain network, as well as the time of response of the blockchain network in case of query of data from it. As part of this work, the blockchain network acts as a tool to log all the changes of the identification system in order to prevent counterfeiting and substitution of the identification data of devices and sensors of the Internet of Things.
Using Natural Language Processing for Phishing Detection

We live in a world where computers are constantly changing the way we do things. People spend many hours on their phones or computers, whether it be for work or leisure purposes. The danger is that these unsuspecting users can be targeted for attacks at any time and can fall victim to many types of scams or phishing attacks. These attacks can be harmful to the user by getting valuable credentials, money or even installing malicious software on their devices, all while the user is unaware of what has just happened. In a business environment these can lead to mass data breeches which could end up costing a company millions of euros. Many users are not trained to recognize phishing texts, so an alternative solution is needed to help prevent users from falling into these traps. In this paper we will be investigating Natural Language Processing (NLP), a subsection of Machine Learning (ML) to try generate solutions to the problem of phishing. We will investigating different NLP solutions: Word2Vec, Doc2Vec and BERT, and different ML solutions: RNN, LSTM, CNN and TD-IDF. All of these different approaches provide good classification results ranging from f1-scores of 90.03–98.94.
Deep Learning Based Matrix Factorization For Collaborative Filtering

Collaborative Filtering based on matrix factorization (MF) has shown tremendous success in the field recommender system. However, MF has difficulty in handling sparsity and scalability. These resulted in low quality of recommendations. In this regard, deep learning has shown immense success in different application areas including recommender systems. To address the limitations, we incorporate deep learning architecture to matrix factorization and develop a novel mode. The core idea of the method is to map users and items input vector to two well-structured deep neural network architectures separately for factorization. Then, we incorporate inner product to the output layers of the network to predict the rating scores. The use of this structure significantly improve the quality of recommendation. The experimental result on real data sets shows that our proposed model outperformed state of the art methods.
Neuroscience and Artificial Intelligence

AI can even outperform humans in many tasks such as winning games like Go and poker as well as engaging in creative endeavours in writing novels and music. Despite this, it is still a long way from building artificial human intelligence. Current AIs are only designed to excel in their intended functions and cannot generate knowledge to new tasks and situations. For AI to achieve artificial human intelligence requires us to study and understand the human brain. Neuroscience is the study of the anatomy and physiology of the human brain. It provides us interesting insights into how the brain works to develop better AI systems. Conversely, better AI systems can help drive neuroscience forward and further unlock the secrets of the brain. Neuroscience and AI are closely related. The synergy of the two will benefit each other. Besides the benefits of neuroscience for AI research, neuroscience also has important implications for machine learning. This chapter discusses the implications of neuroscience for general artificial intelligence and the benefits of AI for neuroscience research.
Reversible Data Hiding in Encrypted Image

Today, digital communication has become an integral part of everyday life. Many applications are based on the Internet and communications must be made in secret. This is especially important when confidential information needs to be exchanged. As a result, the security of information transmitted over public channels is a fundamental problem, and as a result, the confidentiality and integrity of the data is essential to protect against unauthorized access and use. This caused the information concealment area to become unstable. Encryption and steganography are two of the most common methods you can use to ensure safety. With encryption, the data is converted to another form of gibberish and the encrypted data is transmitted. In Steganography, the image is transferred to the image, which is embedded in the image without affecting the quality of the image.This article suggests a new way to embed data into images and edit data using common virtual built-in technologies.
Design of Chinese Smart Kitchen Based on Users' Behavior

The concept of smart kitchens has become a hot topic for Chinese families as IoT technology in smart homes has matured. Current smart kitchen includes a variety of smart products which are controlled via mobile terminals. However, the accumulation of smart items that focus on solving a single pain point reduces the available space in the kitchen, and decreases the efficiency of kitchen cooking. As a result, encouraging the systematization and integration of single-product smart kitchens is a critical design issue to be solved immediately. Based on behavioral research, this paper employs quantitative and qualitative methods to investigate the common behavioral characteristics and pain points of smart kitchen target user groups. According to the study of user behavior, requirements and smart kitchen technology, a systematic design goals of Chinese smart kitchens with emotional communication and all-in-one interaction are proposed, which solves the problems of trivial interaction and fragmented usage scenes in existing smart kitchens, providing constructive meaning for the human-centered design direction of Chinese smart kitchens.
A Novel Method to against Quantum Noises in Quantum Teleportation

In order to improve the anti-noise performance of quantum teleportation, this paper proposes a novel dynamic quantum anti-noise scheme based on the quantum teleportation which transmits single qubit state using Bell state. Considering that quantum noise only acts on the transmitted qubit, i.e., the entangled state that Alice and Bob share in advance is affected by the noise, thus affecting the final transmission result. In this paper, a method for dynamically adjusting the shared entangled state according to the noise environment is proposed. By calculating the maximum fidelity of the output state to determine the shared entangled state, which makes the quantum teleportation be affected by the noise as little as possible. This paper calculates the fidelity of teleportation under four kinds of channel noise (amplitude damping, phase damping, bit flip and depolarizing noise). The results show that the scheme has a suppression effect on phase damping, bit flip and depolarizing noise under certain conditions. When the noise intensity is larger, the optimized efficiency is better.
Evolutionary Deep Reinforcement Learning Environment: Transfer Learning-Based Genetic Algorithm

Stock markets trading has risen as a critical challenge for artificial intelligence research. Such environments require artificial agents to coordinate and transfer their best experience to other agents. However, the strongest agents have been trained using expert capabilities or employing hand-crafted experts features. Notwithstanding these improvements, no previous single system has come near to mastering the trading environment.
Mining Twitter Data on COVID-19 for Sentiment analysis and frequent patterns Discovery (Preprint)

<sec> <title>UNSTRUCTURED</title> A study with a societal objective was carried out on people exchanging on social networks and more particularly on Twitter to observe their feelings on the COVID-19. A dataset of more than 600,000 tweets with hashtags like COVID and coronavirus posted between February 27, 2020 and March 25, 2020 was built. An exploratory treatment of the number of tweets posted by country, by language and other parameters revealed an overview of the apprehension of the pandemic around the world. A sentiment analysis was elaborated on the basis of the tweets posted in English because these constitute the great majority. On the other hand, the FP-Growth algorithm was adapted to the tweets in order to discover the most frequent patterns and its derived association rules, in order to highlight the tweeters insights relatively to COVID-19. </sec>
AdvEdge: Optimizing Adversarial Perturbations Against Interpretable Deep Learning

Deep Neural Networks (DNNs) have achieved state-of-the-art performance in various applications. It is crucial to verify that the high accuracy prediction for a given task is derived from the correct problem representation and not from the misuse of artifacts in the data. Hence, interpretation models have become a key ingredient in developing deep learning models. Utilizing interpretation models enables a better understanding of how DNN models work, and offers a sense of security. However, interpretations are also vulnerable to malicious manipulation. We present AdvEdge and AdvEdge\(^{+}\), two attacks to mislead the target DNNs and deceive their combined interpretation models. We evaluate the proposed attacks against two DNN model architectures coupled with four representatives of different categories of interpretation models. The experimental results demonstrate our attacks’ effectiveness in deceiving the DNN models and their interpreters.
Disconnection and Reconnection as Resistance to Geosurveillance

Digital media has spawned entire industries centered on geosurveillance, resulting in complex flows of sensitive data. Practices surrounding the collection, use, and sale of this data are commonly concealed behind lengthy privacy policies riddled with legal jargon and devoid of technical specificity. Simultaneously, new methods of analysis tease out information from even “anonymized” data. As a result, it increasingly seems that the only reliable shelter from geosurveillance is to disconnect, but how difficult is this in practice, is it worth pursuing, and how might we do so? This chapter examines these questions. It first outlines several conceptualizations of privacy and establishes what is at stake every time privacy is eroded. It then overviews the many mechanisms that can produce geospatial data, illustrating the ubiquity of geosurveillance and difficulty of disconnection. Finally, and despite this difficulty, it discusses tactics for resistance, demonstrating that modern privacy requires not just disconnection, but reconnection.
A Privacy Enhancement Scheme Based on Blockchain and Blind Signature for Internet of Vehicles

In Internet of Vehicles (IoV), it is getting increasing interests how to enhance the privacy of vehicles while commucating with other vehicles or Infrastructres. This paper proposes a vehicle privacy protection scheme based on blockchain and blind signature. Each vehicle has its own private key, and uses it to generate the correspongding public key. Then the vehicle obtains a digital certicate from the certicate authority (CA) through a blind signature algorithm, which ensures that the real identity information of the vehicle is strictly condential. When CA issues a certicate to a vehicle, the public key of the vehicle is stored in the blockchain. In vehicle-to-vehicle communication, the receiver can perform a hash operation with the sender’s public key to obtain a Merkel’s root value, and compare it with the root in the blockchain to confirm the legitimacy of the sender’s identity. Simulation experiment results verify the effectiveness and applicability of the proposed scheme.
A Compact Soft Ordinal Regression Network for Age Estimation

Age estimation is a focal point of computer vision research. With the development of Convolutional Neural Networks (CNNs), some previous studies utilize large-scale models to learn the informative age representations to boost performance. However, these models with a large number of parameters are not suited to the low-power devices. In this paper, we propose a compact model called Soft-Lite ECA RepVGG network (LERep) for age estimation to alleviate this problem. We utilize the re-parameterization technique and the Efficient Channel Attention (ECA) module to construct a novel model. Decoupling the training and inference stage can reduce the computation cost. In other words, the training-time model is a multi-branch model, and then we convert the multi-branch model to a single plain model during inference time. Our approach has been proven to effectively trade off the performance and parameters for age estimation. Moreover, we propose a novel loss that utilizes the ordinal age information and expectation of age label distribution to address the non-stationary aging problem. According to the experimental results on MORPH II, FG-NET and CLAP2015, our models achieve state-of-the-art performance compare with other compact models. In particular, our models also achieve competitive performance compare with over larger models, while our models have approximately 1/250 parameters of the VGG-16 network.
Central Bank Digital Currency and its impact on the Indian Financial system

Cash has been used as a means of exchange for a long time and has been changing its form from paper to paperless. It has been an effective tool in shaping the payment system and acts as an important pillar of the financial system. CBDC (Central Bank digital Currency) is a form of digital currency which is controlled by the country’s central bank and backed by the Government. With the rise of electronic payments and innovations in the financial sector has highlighted the lacking of the existing payments system. Role of central Bank has been questioned since the emergence of the crypto currency like Bitcoin in 2008.Moreover, the monetary authorities around the world are of the view that the society’s trust in the financial system is because of the Central Bank guardianship. There has been an increase in the number of Central banks around the globe from ~60% in 2017 to 80% in 2019 who are looking to come up with a CBDC as published in the report of Bank for International Settlements (BIS) (Todd &amp; Rogers, 2020).The paper aims to study the implication of CBDC on the Indian Financial system.
Machine Learning Algorithms Evaluation for Phishing URLs Classification

Phishing URL is a type of cyberattack, based on falsified URLs. The number of phishing URL attacks continues to increase despite cybersecurity efforts. According to the Anti-Phishing Working Group (APWG), the number of phishing websites observed in 2020 is 1 520 832, doubling over the course of a year. Various algorithms, techniques and methods can be used to build models for phishing URL detection and classification. From our reading, we observed that Machine Learning (ML) is one of the recent approaches used to detect and classify phishing URL in an efficient and proactive way. In this paper, we evaluate eleven of the most adopted ML algorithms such as Decision Tree (DT), Nearest Neighbours (KNN), Gradient Boosting (GB), Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Support Vector Machines (SVM), Neural Network (NN), Ex-tra_Tree (ET), Ada_Boost (AB) and Bagging (B). To do that, we compute detection accuracy metric for each algorithm and we use lexical analysis to extract the URL features.
Automatic Attendance System using Face Recognition

Attendance in an educational institution for students is the most challenging part of the virtual platform. It wastes a lot of effort and time when it is done manually. There was also a lot of fake attendance due to lack of a good attendance system. The face is the most important thing which identifies a human. Therefore, in this paper, we work on an attendance system using a face detection algorithm in real-time using the frontal face recognition concept. In this paper, we describe an efficient algorithm of haar cascade using an open-source image processing framework known as OpenCV.
Chapter 6. Tractable Boolean and Arithmetic Circuits

Tractable Boolean and arithmetic circuits have been studied extensively in AI for over two decades now. These circuits were initially proposed as “compiled objects,” meant to facilitate logical and probabilistic reasoning, as they permit various types of inference to be performed in linear time and a feed-forward fashion like neural networks. In more recent years, the role of tractable circuits has significantly expanded as they became a computational and semantical backbone for some approaches that aim to integrate knowledge, reasoning and learning. In this chapter, we review the foundations of tractable circuits and some associated milestones, while focusing on their core properties and techniques that make them particularly useful for the broad aims of neuro-symbolic AI.