Reviewerly/exHarmony huggingface dataset:
Mobile Keystroke Biometrics Using Transformers

Among user authentication methods, behavioural biometrics has proven to be effective against identity theft as well as user-friendly and unobtrusive. One of the most popular traits in the literature is keystroke dynamics due to the large deployment of computers and mobile devices in our society. This paper focuses on improving keystroke biometric systems on the free-text scenario. This scenario is characterised as very challenging due to the uncontrolled text conditions, the influence of the user's emotional and physical state, and the in-use application. To overcome these drawbacks, methods based on deep learning such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been proposed in the literature, outperforming traditional machine learning methods. However, these architectures still have aspects that need to be reviewed and improved. To the best of our knowl-edge, this is the first study that proposes keystroke biometric systems based on Transformers. The proposed Transformer architecture has achieved Equal Error Rate (EER) values of 3.84% in the popular Aalto mobile keystroke database using only 5 enrolment sessions, outperforming by a large margin other state-of-the-art approaches in the literature.
Unsupervised Face Recognition using Unlabeled Synthetic Data

Over the past years, the main research innovations in face recognition focused on training deep neural networks on large-scale identity-labeled datasets using variations of multi-class classification losses. However, many of these datasets are retreated by their creators due to increased privacy and ethical concerns. Very recently, privacy-friendly synthetic data has been proposed as an alternative to privacy-sensitive authentic data to comply with privacy regulations and to ensure the continuity of face recognition research. In this paper, we propose an unsupervised face recognition model based on unlabeled synthetic data (USynthFace). Our proposed USynthFace learns to maximize the similarity between two augmented images of the same synthetic instance. We enable this by a large set of geometric and color transformations in addition to GAN-based augmentation that contributes to the USynthFace model training. We also conduct numerous empirical studies on different components of our USynthFace. With the proposed set of augmentation operations, we proved the effectiveness of our USynthFace in achieving relatively high recognition accuracies using unlabeled synthetic data. The training code and pretrained model are publicly available under https://github.com/fdbtrs/Unsupervised-Face-Recognition-using-Unlabeled-Synthetic-Data.
CodedPaddedFL and CodedSecAgg: Straggler Mitigation and Secure Aggregation in Federated Learning

We present two novel federated learning (FL) schemes that mitigate the effect of straggling devices by introducing redundancy on the devices’ data across the network. Compared to other schemes in the literature, which deal with stragglers or device dropouts by ignoring their contribution, the proposed schemes do not suffer from the client drift problem. The first scheme, CodedPaddedFL, mitigates the effect of stragglers while retaining the privacy level of conventional FL. It combines one-time padding for user data privacy with gradient codes to yield straggler resiliency. The second scheme, CodedSecAgg, provides straggler resiliency and robustness against model inversion attacks and is based on Shamir’s secret sharing. We apply CodedPaddedFL and CodedSecAgg to a classification problem. For a scenario with 120 devices, CodedPaddedFL achieves a speed-up factor of 18 for an accuracy of 95% on the MNIST dataset compared to conventional FL. Furthermore, it yields similar performance in terms of latency compared to a recently proposed scheme by Prakash et al. without the shortcoming of additional leakage of private data. CodedSecAgg outperforms the state-of-the-art secure aggregation scheme LightSecAgg by a speed-up factor of 6.6–18.7 for the MNIST dataset for an accuracy of 95%.
Deep Learning-Assisted OFDM Channel Estimation and Signal Detection Technology

The orthogonal frequency division multiplexing (OFDM) technique has received wide attention because of its high spectrum utilization. However, the drawback of inter-subcarrier interference in OFDM systems makes the channel estimation and signal detection performance of OFDM systems with few pilots and short cyclic prefixes (CP) poor. In this letter, we use deep learning to assist OFDM in recovering nonlinearly distorted transmission data. Specifically, we use a self-normalizing network (SNN) for channel estimation, combined with a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) for signal detection, thus proposing a novel SNN-CNN-BiGRU network structure (SCBiGNet). The simulation results show that the SCBiGNet model outperforms the existing techniques for the different numbers of pilots and lengths of CPs. The BER performance is improved by 0.2-9 dB.
Blockchain and Metaverse For Peer-to-peer Energy Marketplace: Research Trends and Open Challenges

Blockchain is an innovative technology destined to shape the future of businesses and markets around the globe. It promises the democratization of influence and transparency of information among its members. Academic researchers, as well as companies, are investing time, money, and effort to bring it to the market hoping that they maximize their projects' value. In this article, we take a look at the energy sector, specifically peer-to-peer (P2P) trading, which is moving rapidly towards digitization and integration of cutting-edge technologies, such as blockchain, to become smarter and more efficient like never before. Also, we investigate the feasibility and requirements of a blockchain-based P2P energy marketplace in the metaverse. Then, we discuss the current limitations of the P2P trading systems, the opportunities brought by blockchain to achieve market readiness, the current research trend, the existing pilot projects, and finally the open research challenges.
Improved Variational Quantum Eigensolver Via Quasidynamical Evolution

The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm designed for current and near-term quantum devices. Despite its initial success, there is a lack of understanding involving several of its key aspects. There are problems with VQE that forbid a favourable scaling towards quantum advantage. In order to alleviate the problems, we propose and extensively test a quantum annealing inspired heuristic that supplements VQE. The improved VQE enables an efficient initial state preparation mechanism, in a recursive manner, for a quasi-dynamical unitary evolution. We conduct an in-depth scaling analysis of finding the ground state energies with increasing lattice sizes of the Heisenberg model, employing simulations of up to $40$ qubits that manipulate the complete state vector. For the current devices, we further propose a benchmarking toolkit using a mean-field model and test it on IBM Q devices. The improved VQE avoids barren plateaus, exits local minima, and works with low-depth circuits. Realistic gate execution times estimate a longer computational time to complete the same computation on a fully functional error-free quantum computer than on a quantum computer emulator implemented on a classical computer. However, our proposal can be expected to help accurate estimations of the ground state energies beyond $50$ qubits when the complete state vector can no longer be stored on a classical computer, thus enabling quantum advantage.
Blockchain Adoption among Multi-Stakeholders under Government Subsidy: From the Technology Diffusion Perspective

Despite the well-documented benefits and encouraging policy environment for blockchain technology (BT), its extensive adoption in the construction industry is still unsolved. The interorganizational diffusion of BT is not explored in existing studies, requiring a comprehensive investigation for better government policymaking. Thus, from the technology diffusion perspective, this paper proposes an evolutionary game-based system dynamics (EG-SD) model to describe the complicated relationships between government and construction enterprises in the process of BT adoption. Through theoretical analysis, the main stakeholders, influencing factors, and diffusion channels in the process of BT adoption are identified. Further, a simulation collecting data from China's construction industry is conducted to evaluate the diffusion performance under different scenarios. This study contributes to the body of knowledge by providing a quantitative perspective for understanding BT adoption behaviors among construction enterprises and revealing the diffusion mechanism of BT from a multilevel perspective. Also, this paper for the first time combines evolutionary game theory with system dynamics into explaining BT adoption, providing practical implications to both government and construction enterprises on policymaking and promoting BT adoption practices.
Predictive tracking of an object by a pan–tilt camera of a robot

Abstract Moving-object tracking using a pan–tilt camera setup is quite a well-known task in robotics. However, the presented research addresses specific properties of the tracked object and introduces novel features to the pan–tilt camera control strategy. Pan–tilt camera control does not operate in an isolated environment. It is a part of the visual servoing system with specific goals. The system has to fulfill certain purposes, which affect its configuration and functionality. The pan–tilt system aims at keeping the visually tracked object within the middle of the image. At the same time, the overall visual servoing efficiently recognizes and tracks the object enabling its grasping by the robot arm. It uses a predictive strategy utilizing specific second-order linear models for pan and tilt joints. Model predictive control (MPC) introduces into the system the ability to predict camera operation over the specific horizon according to the predefined tracking goals. As the system anticipates future positions over the horizon of operation, the setpoint prediction of the future tracked system positions is required. Visual object recognition and tracking system use particular strategies for preparing online tracked object extrapolation over MPC horizon. Therefore, the pan–tilt camera system is intrinsically coupled to camera-based recognition and tracking. Predictive pan–tilt positioning keeps the tracked system in the middle of the image, while the visual system extrapolation improves the tracking performance. The proposed approach is thoroughly tested in the dedicated Gazebo-based robot simulator. Finally, the system is implemented and validated on the Velma robot. The results and their comparison with other control strategies confirm the initial assumptions, allowing further visual servoing system development.
Calibrated Q-Matrix-Enhanced Deep Knowledge Tracing with Relational Attention Mechanism

With the development of online educational platforms, numerous research works have focused on the knowledge tracing task, which relates to the problem of diagnosing the changing knowledge proficiency of learners. Deep-neural-network-based models are used to explore the interaction information between students and their answer logs in the current field of knowledge tracing studies. However, those models ignore the impact of previous interactions, including the exercise relation, forget factor, and student behaviors (the slipping factor and the guessing factor). Those models also do not consider the importance of the Q-matrix, which relates exercises to knowledge points. In this paper, we propose a novel relational attention knowledge tracing (RAKT) to track the students’ knowledge proficiency in exercises. Specifically, the RAKT model incorporates the students’ performance data with corresponding interaction information, such as the context of exercises and the different time intervals between exercises. The RAKT model also takes into account the students’ interaction behaviors, including the slipping factor and the guessing factor. Moreover, consider the relationship between exercise sets and knowledge sets and the relationship between different knowledge points in the same exercise. An extension model of RAKT is called the Calibrated Q-matrix relational attention knowledge tracing model (QRAKT), which was developed using a Q-matrix calibration method based on the hierarchical knowledge levels. Experiments were conducted on two public educational datasets, ASSISTment2012 and Eedi. The results of the experiments indicated that the RAKT model and the QRAKT model outperformed the four baseline models.
HTDet: A Hybrid Transformer-Based Approach for Underwater Small Object Detection

As marine observation technology develops rapidly, underwater optical image object detection is beginning to occupy an important role in many tasks, such as naval coastal defense tasks, aquaculture, etc. However, in the complex marine environment, the images captured by an optical imaging system are usually severely degraded. Therefore, how to detect objects accurately and quickly under such conditions is a critical problem that needs to be solved. In this manuscript, a novel framework for underwater object detection based on a hybrid transformer network is proposed. First, a lightweight hybrid transformer-based network is presented that can extract global contextual information. Second, a fine-grained feature pyramid network is used to overcome the issues of feeble signal disappearance. Third, the test-time-augmentation method is applied for inference without introducing additional parameters. Extensive experiments have shown that the approach we have proposed is able to detect feeble and small objects in an efficient and effective way. Furthermore, our model significantly outperforms the latest advanced detectors with respect to both the number of parameters and the mAP by a considerable margin. Specifically, our detector outperforms the baseline model by 6.3 points, and the model parameters are reduced by 28.5 M.
Cult violence in Nigeria and corporate social responsibility in oil-producing communities

Since the beginning of 2014, reports of cult violence have increased sharply in the Nigeria’s oil-producing communities. Hence, we set out to examine the role of corporate social responsibility (CSR) from multinational oil companies (MOCs) in mitigating the spread of cult group violence in the region. A total of two thousand four hundred respondents were sampled across the nine states of the Niger Delta. Results from the use of estimated logit model reveal that MOCs via Global Memorandum of Understanding (GMoU) are in good position to deter the aggression and rise of cult violence using interventions for youth as a priority target group mechanism. This calls for pro-youth capacity building programmes, specially designed to equip the clusters with appropriate skills required for peaceful engagement and to complement government efforts in the planning and implementation of the development agenda for their respective communities. In turn, this will contribute towards enhancing a peaceful environment for doing business in the Niger Delta region.
SMG: A Micro-gesture Dataset Towards Spontaneous Body Gestures for Emotional Stress State Analysis

Abstract We explore using body gestures for hidden emotional state analysis. As an important non-verbal communicative fashion, human body gestures are capable of conveying emotional information during social communication. In previous works, efforts have been made mainly on facial expressions, speech, or expressive body gestures to interpret classical expressive emotions. Differently, we focus on a specific group of body gestures, called micro-gestures (MGs), used in the psychology research field to interpret inner human feelings. MGs are subtle and spontaneous body movements that are proven, together with micro-expressions, to be more reliable than normal facial expressions for conveying hidden emotional information. In this work, a comprehensive study of MGs is presented from the computer vision aspect, including a novel spontaneous micro-gesture (SMG) dataset with two emotional stress states and a comprehensive statistical analysis indicating the correlations between MGs and emotional states. Novel frameworks are further presented together with various state-of-the-art methods as benchmarks for automatic classification, online recognition of MGs, and emotional stress state recognition. The dataset and methods presented could inspire a new way of utilizing body gestures for human emotion understanding and bring a new direction to the emotion AI community. The source code and dataset are made available: https://github.com/mikecheninoulu/SMG .
Deep Learning Opacity in Scientific Discovery

Abstract While philosophers have focused on epistemological and ethical challenges of using artificial intelligence (AI) in science, scientists have focused largely on opportunities. I argue that this disconnect between philosophical pessimism and scientific optimism is driven by failures to critically examine the practice of AI-infused science. To appreciate the epistemic justification for AI-powered breakthroughs, philosophers must analyze the role of AI as part of a wider process of discovery. I demonstrate the importance of this with two cases from the scientific literature, and show that epistemic opacity need not diminish AI’s capacity to lead scientists to significant and justifiable breakthroughs.
Monetisation of digital health data through a GDPR-compliant and blockchain enabled digital health data marketplace: A proposal to enhance patient's engagement with health data repositories

With the growing use of digital technologies, the vision of a predictive, preventive and personalised healthcare system, commonly termed healthcare 4.0, is becoming tangible. However, the manifestation of healthcare 4.0 relies on empowering patients as active stakeholders of the healthcare system and transforming traditionally siloed data repositories into accessible ones. Combining blockchain-like distributed technology and Airbnb-like sharing economy model, this paper proposes a digital health data marketplace (DHDM) through which patients, as the data producer, can sell their health data to data consumers like medical practitioners, researchers, policymakers and machine learning algorithm developers without compromising privacy and security. In conjunction with the Welsh Secure Anonymised Information Linkage (SAIL) Databank, a traditional centralised and siloed data repository, DHDM was evaluated in a simulated environment to understand the technical feasibility, regulation compliance and, most importantly, the impact of such marketplace on healthcare 4.0 transformation.
ECM-EFS: An ensemble feature selection based on enhanced co-association matrix

Currently, feature selection faces a huge challenge that no single feature selection method can effectively deal with various data sets for all real cases. Ensemble learning is a potential promising solution to address this problem. We propose an ensemble feature selection method based on enhanced co-association matrix (ECM-EFS). Positive-co-association matrix (PCM), negative-co-association matrix (NCM), and relative-co-association matrix (RCM) are first introduced to discover the relationship among features by ensembling the results in multiple feature selection methods. To further produce a more stable feature selection result, "Feature Kernel" is also introduced and used as a starting point for feature selection. Comparative experiments with four state-of-the-art methods have confirmed that the ECM-EFS can provide more robust results. Moreover, compared with traditional ensemble feature selection methods, our method can compensate information loss and reduce computational cost significantly.
A New Scalable and Secure Access Control Scheme Using Blockchain Technology for IoT

The growth of IoT devices is so rapid that several billions of such devices would be in use in a span of four-year period. Essential security mechanisms need to be put in place to curb several security attacks prevalent in IoT. Access control is an important security mechanism that ensures legitimate and controlled access to critical and limited resources in IoT. The current access control schemes for IoT could not handle burgeoning number of IoT devices, while meeting the necessary level of security. Consequently, in this paper, we propose a new scalable and secure access control scheme for IoT. With blockchain as the root-of-trust, the proposed scheme performs access control for the IoT devices without having the resource-constrained IoT devices to be part of the blockchain network and to possess substantial amount of blockchain data. Blockchain’s tamper-proof property makes it an ideal candidate to be chosen as the root-of-trust. The scheme is secure against various security attacks prevalent in IoT. A proof-of-concept implementation for the scheme is developed and deployed in Ethereum Mainnet. The transaction costs of the different operations in the scheme are fairly below USD 3. Furthermore, scalability of the proposed scheme in different scenarios is investigated.
Shaping feminist artificial intelligence

This article examines the historical and contemporary shaping of feminist artificial intelligence (FAI). It begins by looking at the microhistory of FAI through the writings of Alison Adam and her graduate students to enrich the plural histories of AI and to write back feminist history into AI. Then, to explore contemporary examples of how FAI is being shaped today and how it deploys a multiplicity of meanings, I provide the following typology: FAI (1) as model, (2) as design, (3) as policy, (4) as culture, (5) as discourse, and (6) as science. This typology sheds light on the following questions: What does the term FAI mean? How has FAI been shaped over time?
Recalibrating the Banking Sector with Blockchain Technology for Effective Anti-Money Laundering Compliances by Banks

The banking sector is identified as the main means for laundering illicit money, these banks generally have access to both banking mechanism and legal authority to make decisions. Money launderers and those financing terrorism are conveniently accessing financial institutions and its mechanism. These institutions provide all financial funds transfers both domestic and international range. Anti-money laundering (AML) laws and other data protection laws that keep getting stricter have forced many financial institutions to put in place long, expensive processes to stay in compliance. To bridge the gap, emerging technology can help in mitigating money laundering and other financial crimes. Blockchain is considered one of the world's best-known examples of Distributed Ledger Technology. In the financial sector, this type of technology has been hailed as the key to future success. Emerging technology can be used in many ways in Financial Services. It can change many processes, payments between peer-to-peer, trade agreements and tracking of supply chains. Emerging technology can be used in many ways in financial services and can change many processes, peer-to-peer payments, trade agreements, and the tracking of supply chains. These use cases depend on the participants or users being identified and verified. "Know Your Customer" is the term for this (KYC). Before making a transaction, one of the most basic ways to build trust between the people involved is to check out the user. This current paper focuses on issues of compliance and Anti Money Laundering policies in the banking sector by using new emerging technologies such as blockchain; further, the paper focuses keenly on issues relating to the manipulation of KYC and the financial burden on banks while also addressing AML policies, Finally, the paper provides ideas and suggestions regarding the rise of emerging technologies such as blockchain while addressing the problems of ML. This also includes the capability of blockchain technology to bring banking systems with recalibrated mode of compliances polices.