Reviewerly/exHarmony huggingface dataset:
Gaussian processes for missing value imputation

A missing value indicates that a particular attribute of an instance of a learning problem is not recorded. They are very common in many real-life datasets. In spite of this, however, most machine learning methods cannot handle missing values. Thus, they should be imputed before training. Gaussian Processes (GPs) are non-parametric models with accurate uncertainty estimates that combined with sparse approximations and stochastic variational inference scale to large data sets. Sparse GPs (SGPs) can be used to get a predictive distribution for missing values. We present a hierarchical composition of sparse GPs that is used to predict the missing values at each dimension using the observed values from the other dimensions. Importantly, we consider that the input attributes to each sparse GP used for prediction may also have missing values. The missing values in those input attributes are replaced by the predictions of the previous sparse GPs in the hierarchy. We call our approach missing GP (MGP). MGP can impute all observed missing values. It outputs a predictive distribution for each missing value that is then used in the imputation of other missing values. We evaluate MGP on one private clinical data set and on four UCI datasets with a different percentage of missing values. Furthermore, we compare the performance of MGP with other state-of-the-art methods for imputing missing values, including variants based on sparse GPs and deep GPs. Our results show that the performance of MGP is significantly better.
DP-Authentication: A novel deep learning based drone pilot authentication scheme

Unmanned Aerial Vehicles (UAVs), also known as drones, have recently been proposed as flying base stations for providing reliable service to IoT devices. However, due to the lack of effective authentication schemes, UAVs are often hijacked by adversaries, which raises a high potential for sensitive information leakage. Therefore, designing a real-time authentication scheme is essential to enhance UAV safety. Up to the present, several works exist about pilot authentication by classifying radio-control signals. As propagating through the open environment, radio-control signals can be sniffed, analyzed, and simulated, posing significant threats to UAV security. For this reason, we propose a novel deep learning-based drone pilot authentication scheme, DP-Authentication, to protect UAVs from malicious radio-manipulated attacks. Specifically, we collect UAV flight data from the onboard PX4 flight stack and feed them into the authentication scheme to validate pilot legal status dynamically. As verified by comprehensive experiments, the proposed authentication scheme can authenticate pilots with an accuracy of 95.24% and detect malicious hijacking with an accuracy of 96.82%. Thanks to the low system overhead, it holds great promise for deployment on the UAV side to monitor pilot legal status in real-time.
Effect of Immersive VR on Communication Patterns in Architectural Design Critiques

Immersive Virtual Reality (iVR) systems hold a promise to affect design behavior by allowing users to experience presence, as they are embodied in the digital display.However, the lack of research articulating how embodiment enabling media change design behavior limits integrating iVR systems in design pedagogy with a well-defined framework.This paper presents the preliminary results from a case study of a work in progress comparing the communication patterns of critique sessions in an architecture studio in two kinds of media: iVR and non-immersive desk-crits.We employed protocol analysis methods to track the distribution of conversational turns and the first occurrence of design issues emerging over the different media.Results show that compared to non-immersive critiques, the iVR had a lower number of conversational turns and an increase in the first occurrence generated by students, indicating support in a learner-centered activity.Elucidating the effect of the communication medium on design behavior provides an empirical foundation for its inclusion in a design theory that encapsulates embodied cognition.
Comparison of pre-trained language models in terms of carbon emissions, time and accuracy in multi-label text classification using AutoML

Since Turkish is an agglutinative language and contains reduplication, idiom, and metaphor words, Turkish texts are sources of information with extremely rich meanings. For this reason, the processing and classification of Turkish texts according to their characteristics is both time-consuming and difficult. In this study, the performances of pre-trained language models for multi-text classification using Autotrain were compared in a 250 K Turkish dataset that we created. The results showed that the BERTurk (uncased, 128 k) language model on the dataset showed higher accuracy performance with a training time of 66 min compared to the other models and the CO2 emission was quite low. The ConvBERTurk mC4 (uncased) model is also the best-performing second language model. As a result of this study, we have provided a deeper understanding of the capabilities of pre-trained language models for Turkish on machine learning.
Dew: A Transparent Constant-Sized Polynomial Commitment Scheme

We construct a polynomial commitment scheme with constant (i.e., independent of the degree) sized evaluation proofs and logarithmic (in the degree) verification time in the transparent setting. To the best of our knowledge, this is the first result achieving this combination of properties. We build our scheme from an inner product commitment scheme with constant-sized proofs but with linear verification time. To improve the verification time to logarithmic for polynomial commitments, we prove a new extremal combinatorial bound. Our constructions rely on groups of unknown order instantiated by class groups. We prove security of our constructions in the Generic Group Model. Compiling known information-theoretic proof systems using our polynomial commitment scheme yields transparent and constant-sized zkSNARKs (Zero-knowledge Succinct Non-interactive ARguments of Knowledge) with logarithmic verification.
Growth effect of trade misinvoicing in Sub-Saharan Africa: the role of governance

Purpose The growing volume of trade misinvoicing in Sub-Saharan Africa (SSA) calls for serious concern, particularly given its effect on macroeconomic fundamentals. Despite the growing body of literature on the growth effect of trade misinvoicing, empirical evidence on the role of governance in moderating the effect is quite scarce, particularly for SSA. The purpose of this paper is to provide insights into the growth effect of trade misinvoicing in SSA as well as the moderating role of governance in this regard. Design/methodology/approach The feasible generalised least square estimator was applied to analyse relevant data, spanning 2009–2018, of 35 SSA countries. Governance indicators were classified into economic, political and institutional governance, and their individual role in moderating the nexus between trade misinvoicing and economic growth was explored. Findings This paper showed the presence of cross-sectional dependence among SSA countries and long-run convergence of the estimated variables. The empirical finding showed that trade misinvoicing has a negative growth effect in the selected SSA countries, but both economic and political governance are crucial in lowering the observed negative growth effect. Practical implications To curtail trade misinvoicing, SSA policymakers should go beyond just designing anti-money laundering policies to effectively implementing the policies for improved growth prospects. More so, the government of each SSA country must devise means of strengthening governance and building effective, accountable and transparent institutional frameworks that will constantly check and discourage trade misinvoicing activities. Originality/value The originality of this paper stems from its novel assessment of the role governance plays in moderating the growth effect of trade misinvoicing in SSA using the feasible generalised least square estimator. It also details the strategies needed to effectively tackle trade misinvoicing.
A coordinate attention enhanced swin transformer for handwriting recognition of Parkinson's disease

Abstract Diagnosing Parkinson's disease (PD) in its early stages is a significant challenge in medicine. Hand tremors and dysgraphia, which are typical early motor symptoms of PD, can manifest for decades before a formal diagnosis is made. Therefore, handwriting analysis has become an important tool for detecting PD. While many machine learning algorithms have been applied in this area, they struggle to capture the subtle changes in handwriting and must describe features from various perspectives. To address these issues, this paper proposes a Coordinate Attention Enhanced Swin Transformer (CAS Transformer) model for PD handwriting recognition. It establishes the long‐term dependence of features on the joint coordinate attention application, which enables the model to more accurately localize the important features of handwriting data and also extract the fuzzy edge features of handwriting images.These characteristics of the CAS Transformer enable it to outperform current advanced deep learning methods in classification, with an accuracy of 92.68% in experiments conducted on two handwritten datasets.
Federated learning: Applications, Security hazards and Defense measures

Federated learning(FL), a cutting-edge method of distributed learning, enables multiple users to share training results while maintaining the privacy of their personal data. Collecting data from different data owners for making machine learning predictions becomes increasingly challenging as data security becomes more of a priority. Federated learning protects user’s privacy in addition to increase the training data while overcoming the challenges faced by machine learning and deep learning models. Since the data privacy and security is a world-wide concern, the concept of federated learning is increasing day by day from theoretical to practical level. This review paper involves the overview of the federated learning framework, its types, different applications, several types of attacks and defense mechanism.
Official International Mahjong: A New Playground for AI Research

Games have long been benchmarks and testbeds for AI research. In recent years, with the development of new algorithms and the boost in computational power, many popular games played by humans have been solved by AI systems. Mahjong is one of the most popular games played in China and has been spread worldwide, which presents challenges for AI research due to its multi-agent nature, rich hidden information, and complex scoring rules, but it has been somehow overlooked in the community of game AI research. In 2020 and 2022, we held two AI competitions of Official International Mahjong, the standard variant of Mahjong rules, in conjunction with a top-tier AI conference called IJCAI. We are the first to adopt the duplicate format in evaluating Mahjong AI agents to mitigate the high variance in this game. By comparing the algorithms and performance of AI agents in the competitions, we conclude that supervised learning and reinforcement learning are the current state-of-the-art methods in this game and perform much better than heuristic methods based on human knowledge. We also held a human-versus-AI competition and found that the top AI agent still could not beat professional human players. We claim that this game can be a new benchmark for AI research due to its complexity and popularity among people.
RAIH-Det: An End-to-End Rotated Aircraft and Aircraft Head Detector Based on ConvNeXt and Cyclical Focal Loss in Optical Remote Sensing Images

In airport ground-traffic surveillance systems, the detection of an aircraft and its head (AIH) is an important task in aircraft trajectory judgment. However, accurately detecting an AIH in high-resolution optical remote sensing images is a challenging task due to the difficulty in effectively modeling the features of aircraft objects, such as changes in appearance, large-scale differences, complex compositions, and cluttered background. In this paper, we propose an end-to-end rotated aircraft and aircraft head detector (RAIH-Det) based on ConvNeXt-T (Tiny) and cyclical local loss. Firstly, a new U-shaped network based on ConvNeXt-T with the same performance as the Local Vision Transformer (e.g., Swin Transformer) is presented to assess the relationships among aircraft in the spatial domain. Then, in order to enhance the sharing of more mutual information, the extended BBAVectors with six vectors captures the oriented bounding box (OBB) of the aircraft in any direction, which can assist in head keypoint detection by exploiting the relationship between the local and overall structural information of aircraft. Simultaneously, variant cyclical focal loss is adopted to regress the heatmap location of keypoints on the aircraft head to focus on more reliable samples. Furthermore, to perform a study on AIH detection and simplify aircraft head detection, the OBBs of the “plane” category in the DOTA-v1.5 dataset and the corresponding head keypoints annotated by our volunteers were integrated into a new dataset called DOTA-Plane. Compared with other state-of-the-art rotated object and keypoint detectors, RAIH-Det, as evaluated on DOTA-Plane, offered superior performance.
Error suppression by a virtual two-qubit gate

Sparse connectivity of a superconducting quantum computer results in the large experimental overheads of SWAP gates. In this study, we consider employing a virtual two-qubit gate (VTQG) as an error suppression technique. The VTQG enables a non-local operation between a pair of distant qubits using only single qubit gates and projective measurements. Here, we apply the VTQG to the digital quantum simulation of the transverse-field Ising model on an IBM quantum computer to suppress the errors due to the noisy two-qubit operations. We present an effective use of VTQG, where the reduction of multiple SWAP gates results in increasing the fidelity of the output states. The obtained results indicate that the VTQG can be useful for suppressing the errors due to the additional SWAP gates. Additionally, by combining a pulse-efficient transpilation method with the VTQG, further suppression of the errors is observed. In our experiments, we have observed one order of magnitude improvement in accuracy for the quantum simulation of the transverse-field Ising model with 8 qubits.
Neuron-by-Neuron Quantization for Efficient Low-Bit QNN Training

Quantized neural networks (QNNs) are widely used to achieve computationally efficient solutions to recognition problems. Overall, eight-bit QNNs have almost the same accuracy as full-precision networks, but working several times faster. However, the networks with lower quantization levels demonstrate inferior accuracy in comparison to their classical analogs. To solve this issue, a number of quantization-aware training (QAT) approaches were proposed. In this paper, we study QAT approaches for two- to eight-bit linear quantization schemes and propose a new combined QAT approach: neuron-by-neuron quantization with straight-through estimator (STE) gradient forwarding. It is suitable for quantizations with two- to eight-bit widths and eliminates significant accuracy drops during training, which results in better accuracy of the final QNN. We experimentally evaluate our approach on CIFAR-10 and ImageNet classification and show that it is comparable to other approaches for four to eight bits and outperforms some of them for two to three bits while being easier to implement. For example, the proposed approach to three-bit quantization of the CIFAR-10 dataset results in 73.2% accuracy, while baseline direct and layer-by-layer result in 71.4% and 67.2% accuracy, respectively. The results for two-bit quantization for ResNet18 on the ImageNet dataset are 63.69% for our approach and 61.55% for the direct baseline.
A Semi-Quantum Secret-Sharing Protocol with a High Channel Capacity

Semi-quantum cryptography communication stipulates that the quantum user has complete quantum capabilities, and the classical user has limited quantum capabilities, only being able to perform the following operations: (1) measuring and preparing qubits with a Z basis and (2) returning qubits without any processing. Secret sharing requires participants to work together to obtain complete secret information, which ensures the security of the secret information. In the semi-quantum secret sharing (SQSS) protocol, the quantum user Alice divides the secret information into two parts and gives them to two classical participants. Only when they cooperate can they obtain Alice's original secret information. The quantum states with multiple degrees of freedom (DoFs) are defined as hyper-entangled states. Based on the hyper-entangled single-photon states, an efficient SQSS protocol is proposed. The security analysis proves that the protocol can effectively resist well-known attacks. Compared with the existing protocols, this protocol uses hyper-entangled states to expand the channel capacity. The transmission efficiency is 100% higher than that of single-degree-of-freedom (DoF) single-photon states, providing an innovative scheme for the design of the SQSS protocol in quantum communication networks. This research also provides a theoretical basis for the practical application of semi-quantum cryptography communication.
Exploring 3D Interaction with Gaze Guidance in Augmented Reality

Recent research based on hand-eye coordination has shown that gaze could improve object selection and translation experience under certain scenarios in AR. However, several limitations still exist. Specifically, we investigate whether gaze could help object selection with heavy 3D occlusions and help 3D object translation in the depth dimension. In addition, we also investigate the possibility of reducing the gaze calibration burden before use. Therefore, we develop new methods with proper gaze guidance for 3D interaction in AR, and also an implicit online calibration method. We conduct two user studies to evaluate different interaction methods and the results show that our methods not only improve the effectiveness of occluded objects selection but also alleviate the arm fatigue problem significantly in the depth translation task. We also evaluate the proposed implicit online calibration method and find its accuracy comparable to standard 9 points explicit calibration, which makes a step towards practical use in the real world.
Towards Outdoor Collaborative Mixed Reality: Lessons Learnt from a Prototype System

Most research on collaborative mixed reality (CMR) has focused on indoor spaces. In this paper, we present our ongoing work aimed at investigating the potential of CMR in outdoor spaces. These spaces present unique challenges due to their larger and more com-plex nature, particularly in terms of reconstruction, tracking, and interaction. Our prototype system utilises a photorealistic model to facilitate collaboration between remote virtual reality (VR) users and a local augmented reality (AR) user. We discuss our design considerations, lessons learnt, and areas for future work.
Speed/Area-Efficient ECC Processor Implementation Over GF(2<i> <sup>m</sup> </i>) on FPGA via Novel Algorithm-Architecture Co-Design

With the rapid evolution of security technology, small field-size elliptic curve-based point multiplication (PM) has gradually become obsolete, leading to the implementation of PM with large field sizes. From this perspective, in this article, through a novel algorithm-architecture co-design strategy, we propose an efficient implementation of the PM on the elliptic curve over GF( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{m}$ </tex-math></inline-formula> ) (particularly targeting large field sizes). To achieve an area-time-efficient elliptic curve cryptography (ECC) processor implementation on the field-programmable gate array (FPGA) platform, we have proposed a bottom-up approach based on three coherent interdependent layers of efforts. First, we proposed an efficient digit-serial versatile multiplier (DSVM) based on polynomial representation. The system is built using the four-way overlap-free Karatsuba algorithm (OFKA) and a modified radix-n interleaved multiplication (mRnIM) technique (for area and time complexities reduction). Of course, the efficiency of the proposed multiplier is demonstrated by the complexity analysis and comparison with the existing reported designs. Second, we have adopted the López–Dahab (LD) Montgomery PM algorithm to avoid data dependency and enhance signal control in the ECC design. Meanwhile, a series of resource optimization techniques have also been adopted for the proposed ECC processor to optimize the overall design efficiency further. Third, the proposed ECC PM architecture is then implemented on the FPGA platform, showing that the proposed ECC crypto-processor obtains the least area-delay product (ADP) among all the existing structures for the large field sizes.
Photonic Binary Convolutional Neural Network Based on Microring Resonator Array

We propose the photonic architecture based on microring resonator (MRR) arrays for binary convolutional neural networks (BCNNs) accelerated computing. The MRR crossbar unit is used for computing weight {−1, 1} and the single MRR is for input {0, 1}. The computing parallelism is improved through wavelength division multiplexing. The photonic BCNN achieves 97.29% classification accuracy on the MNIST test set which is only 1.94% lower than the accuracy of the 32-bit neural network, and saves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$32\times $ </tex-math></inline-formula> at memory usage. We analyze effects of input and weight encoding errors on the photonic BCNN. When the input or weight error rate is less than 0.01%, the test accuracy remains unchanged. We evaluate the performance of the photonic BCNN architecture considering optical loss, inter-channel crosstalk, operation frequency and device power consumption. The energy efficiency of the designed photonic BCNN architecture is 1.72 pJ/MAC, which is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.80\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$61.32\times $ </tex-math></inline-formula> better than the 8-bit and 16-bit architecture respectively. The photonic BCNN is promising to be used for edge computing.
Round-Optimal Oblivious Transfer and MPC from Computational CSIDH

We present the first round-optimal and plausibly quantum-safe oblivious transfer (OT) and multi-party computation (MPC) protocols from the computational CSIDH assumption – the weakest and most widely studied assumption in the CSIDH family of isogeny-based assumptions. We obtain the following results: Prior plausibly quantum-safe isogeny-based OT protocols (with/without setup assumptions) are either not round-optimal, or rely on potentially stronger assumptions. We also build a 3-round maliciously-secure OT extension protocol where each base OT protocol requires only 4 isogeny computations. In comparison, the most efficient isogeny-based OT extension protocol till date due to Lai et al. [Eurocrypt 2021] requires 12 isogeny computations and 4 rounds of communication, while relying on the same assumption as our construction, namely the reciprocal CSIDH assumption.