Reviewerly/exHarmony huggingface dataset:
Towards the Characterization of Realistic Model Generators using Graph Neural Networks

The automatic generation of software models is an important element in many software and systems engineering scenarios such as software tool certification, validation of cyber-physical systems, or benchmarking graph databases. Several model generators are nowadays available, but the topic of whether they generate realistic models has been little studied. The state-of-the-art approach to check the realistic property in software models is to rely on simple comparisons using graph metrics and statistics. This generates a bottleneck due to the compression of all the information contained in the model into a small set of metrics. Furthermore, there is a lack of interpretation in these approaches since there are no hints of why the generated models are not realistic. Therefore, in this paper, we tackle the problem of assessing how realistic a generator is by mapping it to a classification problem in which a Graph Neural Network (GnN) will be trained to distinguish between the two sets of models (real and synthetic ones). Then, to assess how realistic a generator is we perform the Classifier Two-Sample Test (C2ST). Our approach allows for interpretation of the results by inspecting the attention layer of the GNN. We use our approach to assess four state-of-the-art model generators applied to three different domains. The results show that none of the generators can be considered realistic.
A Quantum Blind Multi-Signature Method for the Industrial Blockchain

Traditional anti-quantum methods and multi-signature technologies to secure the blockchain against quantum attacks will quickly reduce the efficiency and scalability of the industrial blockchain, where the computational resources will experience a polynomial rise with the increasing number of traders. Here, a quantum blind multi-signature method is proposed for the multi-party transaction to provide anti-quantum security. First, the proposed multi-party transaction frame and quantum key distribution in the industrial blockchain are introduced. It integrates a novel quantum blind multi-signature algorithm that is based on the quantum entanglement mechanism, and it is absolutely secure in theory. Second, the anti-quantum multi-signature algorithm is illustrated, where there are four phases, i.e., initialization, signing, verification, and implementation. Third, the security and complexity of the proposed framework are analyzed and compared with related methods in references, and our proposed method is verified to be able to offer good computational performance and blockchain scalability for multi-party transaction. Last, the paper is summarized and future research directions are proposed.
Over-the-Air Federated Learning with Retransmissions

Federated Learning (FL) is a distributed machine learning technique designed to utilize the distributed datasets collected by our mobile and internet-of-things devices. As such, it is natural to consider wireless communication for FL. In wireless networks, Over-the-Air Computation (AirComp) can accelerate FL training by harnessing the interference of uplink gradient transmissions. However, since AirComp utilizes analog transmissions, it introduces an inevitable estimation error due to channel fading and noise. In this paper, we propose retransmissions as a method to reduce such estimation errors and thereby improve the FL classification accuracy. First, we derive the optimal power control scheme with retransmissions. Then we investigate the performance of FL with retransmissions analytically and find an upper bound on the FL loss function. The analysis indicates that our proposed retransmission scheme improves both the final classification accuracy after convergence and the convergence speed per communication round. Experimental results demonstrate that the introduction of retransmissions can give higher classification accuracy than one-shot uplink transmissions, without incurring extra communication costs or latency.
Age Group Classification using Convolutional Neural Network (CNN)

Abstract Age group classification is a complex task that is used to classify facial images or videos into predetermined age categories. It is an important task due to its numerous applications such as health, security, authentication system, recruitment, and also in intelligent social robots. Convolutional Neural Network (CNN) has recently shown excellent performance in analysing human face images and videos. This paper proposed an age group classification task using CNN that trained and tested with an All-Age Face (AAF) dataset. FaceNet deep learning model that uses CNN was applied in this study to compute a 128-d embedding that quantifies the face of the age group. The experiment included two age groups: Adolescence and Mature Adulthood. The proposed age group classification model achieved 84.90% accuracy for the training images and 85.12% accuracy for the test images. The experimental results showed that CNN is capable of achieving competitive classification accuracy throughout two age groups in the AAF dataset with unbalanced data distribution.
Design and Experiment of a New Wave Power Conversion Device for Self-Powered Sensor Buoy

The power supply system design of a marine data buoy system is a challenge. Due to cost reasons, it is difficult for marine buoys to obtain electricity directly from the power grid. At present, marine buoys use solar energy, wind energy or wave energy to supply power to sensors. Among them, the employment of wave energy and solar energy has broad application prospects due to the complementary power production of these sources. This paper presents a new wave energy conversion (WEC) device for a marine buoy, which is composed of a floating body and a power generation device. In addition, a three-dimensional finite element simulation model of the buoy movement is established, and the simulation is carried out using hydrodynamic software. On the basis of simulation, experiments are carried out on a 50 kg core power generation unit to estimate the power generation performance of the proposed WEC device.
The Owl: Immersive Telepresence Communication for Hybrid Conferences

The Owl is an immersive telepresence communication system in which remote attendees feel teleported to a physical location where a capture/communication device is placed. During the session they can interact naturally with the people present in that location and also with the other remote participants. Remote attendees get the full immersive experience using commercial VR goggles or Android phones with cardboard-like HMDs. It is also possible to engage in the experience using Android phones or tablets without HMD; in this case the gaze in the remote scenario is controlled using the user’s fingers on the screen. Remote attendees using HMD are represented through avatars in the scene, while participants using plain phones or tablets will get their real time video captured by the device and rendered in overlayed windows. The latter can also be used to provide the local attendees (with) a real-time view of both the remote participants and their local surroundings. Client Software can be downloaded and installed by any user on standard devices.
Serial vs. Parallel Turbo-Autoencoders and Accelerated Training for Learned Channel Codes

Attracted by its scalability towards practical code-word lengths, we revisit the idea of Turbo-autoencoders for end-to-end learning of PHY-Layer communications. For this, we study the existing concepts of Turbo-autoencoders from the literature and compare the concept with state-of-the-art classical coding schemes. We propose a new component-wise training algorithm based on the idea of Gaussian a priori distributions that reduces the overall training time by almost a magnitude. Further, we propose a new serial architecture inspired by classical serially concatenated Turbo code structures and show that a carefully optimized interface between the two component autoencoders is required. To the best of our knowledge, these serial Turbo autoencoder structures are the best known neural network based learned sequences that can be trained from scratch without any required expert knowledge in the domain of channel codes.
Regularized Phrase-Based Topic Model for Automatic Question Classification With Domain-Agnostic Class Labels

Classification of questions according to domain-agnostic class labels relies on a suitable feature extraction process. We propose the use of phrases that are more effective than words to represent questions. The proposed phrase-based topic modeling technique employs asymmetric priors that are scaled with a new C-value for nested regular expressions. In addition, to suppress high-frequency words in phrases, we deploy term weightages computed using the modified distinguishing feature selector. The proposed approach also incorporates a new topic regularization mechanism to facilitate efficient mapping of questions to class labels. We validate the performance of the above approach via four datasets across different domain-agnostic class labels comprising question types, reasoning capabilities, and cognitive complexities. Results obtained highlight that the proposed technique outperforms existing methods in terms of macro-average F1 score.
Multimedia Network English Reading Teaching Model Based on Speech Recognition Confidence Learning Algorithm

With deepening internationalization, English has become an increasingly important communication tool. Because traditional English teaching has short teacher-student interaction time, lack of oral English training environment, and single learning method, the oral English teaching is not ideal, and the students’ “speaking” confidence is insufficient. Aimed at addressing the exposed problems of traditional English reading teaching, this paper proposes a multimedia-based English reading teaching mode. On this basis, establish a voice recognition phoneme network grid to detect the recognition results. Secondly, the lattice is used to generate the confusion network mesh, and the acoustic posterior probability is calculated. Then, the feature vector is input into the SVM classifier for confidence mark, and finally the feature is extracted by principal component analysis. The research shows that multimedia network teaching can teach more vividly, increasing the initiative of students. At the same time, it is shown that the speech recognition confidence learning algorithm can improve the language learning system.
Target-free calibration of flat refractive imaging systems using two-view geometry

Imaging through refractive media is challenged by the alteration of light trajectory from their linear path, thus incurring severe distortions. These distortions are depth-dependent, non-linear, and, more importantly, alter the standard single viewpoint geometry into an axial form. Though modeling such imaging systems has been studied in the past, an in-depth analysis of their two-view geometry is still lacking. We demonstrate in this paper that the value of such form is not limited to the estimation of the relative motion as it allows to calibrate the system independent of any scene knowledge. Therefore, we relieve the establishment of external reference frames and estimate the system parameters through the course of the mission and on-the-fly. In our development, we identify relations among the parameters that allow us to separate them into two orthogonal groups. Using these relations we calibrate the system and solve the pose components. Only a minimal set of five corresponding points is needed for that purpose. We also show that while existing solutions constrain subsets of the parameters or apply hardware installations, our model requires no additional information, still estimating all parameters simultaneously and directly. In the paper, the formulation of two common imaging setups are developed, the first is of a single camera acquiring data through motion, and the other is of a stereo-rig, where the relative pose is fixed but where the system model is more involved. Numerical and experimental results demonstrate that the proposed calibration model exhibits stability to the presence of high levels of noise and achieves high levels of accuracy. Furthermore, we demonstrate how our modeling improves the accuracy by an order of magnitude or more compared to the state-of-the-art.
Verifying Security Vulnerabilities for Blockchain-based Smart Contracts

In a modern world, aspects of cybersecurity become more of a requirement to software, systems, applications than just a feature implemented by programmers in their spare time. On the one hand, blockchain remains a pastime for people interested in digital currencies or decentralized, anonymous environments such as auctions or voting. On the other hand, cyberattacks are also not an exception to the blockchain community. Most of those attacks were made through smart contracts - pieces of code through which blockchain users interact with the actual blockchain. This paper analyses the background of blockchain technology, the implementation of smart contracts, and the cybersecurity aspect in the blockchain field. We describe an in-depth analysis of five static analysis tools (or code verifiers), their capabilities and drawbacks. These are tested with smart contracts with vulnerabilities deliberately included in their source code. The vulnerabilities are tailored so that they fit into the cybersecurity properties. After the implementation process, analysis is presented. We have found out which state-of-the-art static analysis tool is the most appropriate to secure the smart contract code from future cyberattacks on the blockchain.
TAIT: One-Shot Full-Integer Lightweight DNN Quantization via Tunable Activation Imbalance Transfer

Both parameter quantization and depthwise convolution are essential measures to provide high-accuracy, lightweight, and resource-friendly solutions when deploying deep neural networks (DNNs) onto edge-AI devices. However, combining the two methodologies may lead to adverse effects: It either suffers from significant accuracy loss or long finetuning time. Besides, contemporary quantization methods are only selectively applied to weight and activation values but not bias and scaling factor values, making them less practical for ASIC/FPGA accelerators. To solve these issues, we propose a novel quantization framework that is effectively optimized for depthwise convolution networks. We discover that the uniformity of the value range within a tensor can serve as a predictor for the tensor’s quantization error. Under the guidance of this predictor, we develop a mechanism called Tunable Activation Imbalance Transfer (TAIT), which tunes the value range uniformity between an activated feature map and its latter weights. Moreover, TAIT fully supports full-integer quantization. We demonstrate TAIT on SkyNet and deploy it on FPGA. Compared to the state-of-the-art, our quantization framework and system design achieve 2.2%+ IoU, $2.4 \times$ speed, and $1.8 \times$ energy efficiency improvements, without any requirement of finetuning.
Flexible foveated imaging using a single Risley-prism imaging system

Foveated imaging, which has the ability to provide overall situational awareness over a large field of view and high-resolution perception of local details, has significant advantages in many specific applications. However, existing artificially foveated imaging systems are complex, bulky, and expensive, and the flexibility of the fovea specifically has many limitations. To overcome these deficiencies, this paper proposes a method for foveated imaging by collecting multiple partially overlapping sub-fields of view. To capture the above special sub-fields of view, we propose a high-efficiency algorithm based on the characteristics of the field of view deflected by the Risley-prism and aimed at solving the prism rotation angles. In addition, we prove the reliability of the proposed algorithm by cross-validation with the particle swarm optimization algorithm. The experimental results show that the proposed method can achieve flexible foveated imaging using a single Risley-prism imaging system.
Boosting Fairness for Masked Face Recognition

Face recognition achieved excellent performance in re-cent years. However, its potential for unfairness is raising alarm. For example, the recognition rate for the special group of East Asian is quite low. Many efforts have spent to improve the fairness of face recognition. During the COVID-19 pandemic, masked face recognition is becoming a hot topic but brings new challenging for fair face recognition. For example, the mouth and nose are important to recognizing faces of Asian groups. Masks would further reduce the recognition rate of Asian faces. To this end, this paper proposes a fair masked face recognition system. First, an appropriate masking method is used to generate masked faces. Then, a data re-sampling approach is employed to balance the data distribution and reduce the bias based on the analysis of training data. Moreover, we propose an asymmetric-arc-loss which is a combination of arc-face loss and circle-loss, it is useful for increasing recognition rate and reducing bias. Integrating these techniques, this paper obtained fairer and better face recognition results on masked faces.
Robust experience replay sampling for multi-agent reinforcement learning

Learning from the relevant experiences leads to fast convergence if the experiences provide useful information. We present the new and simple yet efficient technique to find suitable samples of experiences to train the agents in a given state of an environment. We intended to increase the number of states visited and unique sequences that efficiently reduce the number of states the agents have to explore or exploit. Our technique implicitly introduces additional strength to the exploration-exploitation trade-off. It filters the samples of experiences that can benefit more than half the number of agents and then utilizes the experiences to extract useful information for decision making. First, we compute the similarities between the observed state and previous states in the experiences to achieve this filtering. Then, we filter the samples using the hyper-parameter, z, to decide which experiences will be suitable. We found out that agents learn quickly and efficiently since sampled experiences provide useful information that speeds up convergence. In every episode, most agents learn or contribute to improve the total expected future return. We further study our approaches' generalization ability and present different settings to show significant improvements in diverse experiment environments.
Suspicious Human Activity Detection Using Pose Estimation and LSTM

Recognition of both human abnormal activity and daily activity is achieved distinctly with skeleton inputs where geometrical relations of skeletal joint coordinates are observed. These handcrafted features play a significant role to enhance performance. In this paper, the key contribution is that the datasets are reduced to eliminate the complexity of the model by implementing RNN-based approaches. 11,376 skeleton features are reduced to 5 geometrical features selected from 40 different subjects and 12 different action classes of daily and health-related actions. Further, we also introduce an LSTM model to classify medical suspicious activities. The set of relevant geometrical attributes are carefully chosen to train the LSTM model. Experimental work is performed on the multilayer LSTM framework to have a good impact on performance as human activity has a greater diversity. The relations between geometrical different datasets such as selected lines and distances between relevant coordinates of joints are evaluated using NTUD 60 dataset. Also, it is demonstrated that less training data is required for classification between normal and medical activity. The Joint-Joint Distance features show 89.07% accuracy on cross-view evaluation and 78.31% accuracy on cross-subject evaluation.
BoxeR: Box-Attention for 2D and 3D Transformers

In this paper, we propose a simple attention mechanism, we call Box-Attention. It enables spatial interaction between grid features, as sampled from boxes of interest, and improves the learning capability of transformers for several vision tasks. Specifically, we present BoxeR, short for Box Transformer, which attends to a set of boxes by predicting their transformation from a reference window on an input feature map. The BoxeR computes attention weights on these boxes by considering its grid structure. Notably, BoxeR-2D naturally reasons about box information within its attention module, making it suitable for end-to-end instance detection and segmentation tasks. By learning invariance to rotation in the box-attention module, BoxeR-3D is capable of generating discriminative information from a bird's-eye view plane for 3D end-to-end object detection. Our experiments demonstrate that the proposed BoxeR-2D achieves state-of-the-art results on COCO detection and instance segmentation. Besides, BoxeR-3D improves over the end-to-end 3D object detection baseline and already obtains a compelling performance for the vehicle category of Waymo Open, without any class-specific optimization. Code is available at https://github.com/kienduynguyen/BoxeR.
Histogram binning revisited with a focus on human perception

This paper presents a quantitative user study to evaluate how well users can visually perceive the underlying data distribution from a histogram representation. We used different sample and bin sizes and four different distributions (uniform, normal, bimodal, and gamma). The study results confirm that, in general, more bins correlate with fewer errors by the viewers. However, upon a certain number of bins, the error rate cannot be improved by adding more bins. By comparing our study results with the outcomes of existing mathematical models for histogram binning (e.g., Sturges’ formula, Scott’s normal reference rule, the Rice Rule, or Freedman–Diaconis’ choice), we can see that most of them overestimate the number of bins necessary to make the distribution visible to a human viewer.
Experimental demonstration of efficient high-dimensional quantum gates with orbital angular momentum

Abstract Quantum gates are essential for the realization of quantum computer and have been implemented in various types of two-level systems. However, high-dimensional quantum gates are rarely investigated both theoretically and experimentally even that high-dimensional quantum systems exhibit remarkable advantages over two-level systems for some quantum information and quantum computing tasks. Here we experimentally demonstrate the four-dimensional X gate and its unique higher orders with the average conversion efficiency 93%. All these gates are based on orbital-angular-momentum degree of freedom (DoF) of single photons. Besides, a set of controlled quantum gates is implemented by use of polarization DoF. Our work is an important step toward the goal of achieving arbitrary high-dimensional quantum circuit and paves a way for the implementation of high-dimensional quantum communication and computation.