Reviewerly/exHarmony huggingface dataset:
Signatures from Sequential-OR Proofs

OR-proofs enable a prover to show that it knows the witness for one of many statements, or that one out of many statements is true. OR-proofs are a remarkably versatile tool, used to strengthen security properties, design group and ring signature schemes, and achieve tight security. The common technique to build OR-proofs is based on an approach introduced by Cramer, Damgård, and Schoenmakers (CRYPTO'94), where the prover splits the verifier's challenge into random shares and computes proofs for each statement in parallel. In this work we study a different, less investigated OR-proof technique, highlighted by Abe, Ohkubo, and Suzuki (ASIACRYPT'02). The difference is that the prover now computes the individual proofs sequentially. We show that such sequential OR-proofs yield signature schemes which can be proved secure in the non-programmable random oracle model. We complement this positive result with a black-box impossibility proof, showing that the same is unlikely to be the case for signatures derived from traditional OR-proofs. We finally argue that sequential-OR signature schemes can be proved secure in the quantum random oracle model, albeit with very loose bounds and by programming the random oracle.
Twin-field quantum digital signatures

Digital signature is a key technique in information security, especially for identity authentications. Compared with classical correspondence, quantum digital signatures (QDSs) provide a considerably higher level of security, i.e., information-theoretic security. At present, its performance is limited by key generation protocols (e.g., BB84 or measurement-device-independent protocols), which are fundamentally limited in terms of channel capacity. Fortunately, the recently proposed twin-field quantum key distribution can overcome this limit. This paper presents a twin-field QDS protocol and details a corresponding security analysis. In its distribution stage, a specific key generation protocol, the sending-or-not-sending twin-field protocol, has been adopted and full parameter optimization method has been implemented. Numerical simulation results show that the new protocol exhibits outstanding security and practicality compared with all other existing protocols. Therefore, the new protocol paves the way toward real-world applications of QDSs
Person Attribute Recognition by Sequence Contextual Relation Learning

Person attribute recognition aims to identify the attribute labels from the pedestrian images. Extracting contextual relation from the images and attributes, including the spatial-semantic relations, the spatial context and the semantic correlation, is beneficial to enhance the discrimination of the features for recognizing the attributes. Thus, this work proposes a sequence contextual relation learning (SCRL) method to capture these relations. It first embeds the images and attributes into sequences in two branches. Then SCRL flexibly learns the contextual relation from the sequences with the parallel attention model structure, which integrates the inter-attention and intra-attention models. The inter-attention module is utilized to extract the spatial-semantic relations, while the intra-attention is designed to gain the spatial context and the semantic correlation. Both attention modules are comprised of several parallel attention units and each unit can obtain the pairwise relations in one subspace. Therefore, they obtain the relations in multiple subspaces, which can improve the comprehensiveness of the relation learning. Additionally, for the sake of better extraction of spatial-semantic relations, this paper employs connectionist temporal classification (CTC) loss which is capable of driving the network to enforce monotonic alignment between the image and attribute. It can also accelerate the convergence of the network by the algorithm in it. Extensive experiments on five public datasets, i.e., Market-1501 attribute, Duke attribute, PETA, RAP and PA-100K datasets, demonstrate the effectiveness of the proposed method.
Utilizing Amari-Alpha Divergence to Stabilize the Training of Generative Adversarial Networks

Generative Adversarial Nets (GANs) are one of the most popular architectures for image generation, which has achieved significant progress in generating high-resolution, diverse image samples. The normal GANs are supposed to minimize the Kullback-Leibler divergence between distributions of natural and generated images. In this paper, we propose the Alpha-divergence Generative Adversarial Net (Alpha-GAN) which adopts the alpha divergence as the minimization objective function of generators. The alpha divergence can be regarded as a generalization of the Kullback-Leibler divergence, Pearson χ 2 divergence, Hellinger divergence, etc. Our Alpha-GAN employs the power function as the form of adversarial loss for the discriminator with two-order indexes. These hyper-parameters make our model more flexible to trade off between the generated and target distributions. We further give a theoretical analysis of how to select these hyper-parameters to balance the training stability and the quality of generated images. Extensive experiments of Alpha-GAN are performed on SVHN and CelebA datasets, and evaluation results show the stability of Alpha-GAN. The generated samples are also competitive compared with the state-of-the-art approaches.
Unsupervised Mitochondria Segmentation in EM Images via Domain Adaptive Multi-Task Learning

Semantic segmentation is an essential step for electron microscopy (EM) image analysis. Although supervised models have achieved significant progress, the need for labor intensive pixel-wise annotation is a major limitation. To complicate matters further, supervised learning models may not generalize well on a novel dataset due to domain shift. In this study, we introduce an adversarial-prediction guided multi-task network to learn the adaptation of a well-trained model for use on a novel unlabeled target domain. Since no label is available on target domain, we learn an encoding representation not only for the supervised segmentation on source domain but also for unsupervised reconstruction of the target data. To improve the discriminative ability with geometrical cues, we further guide the representation learning by multi-level adversarial learning in semantic prediction space. Comparisons and ablation study on public benchmark demonstrated state-of-the-art performance and effectiveness of our approach.
STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing

Predictive scene parsing is a task of assigning pixellevel semantic labels to a future frame of a video. It has many applications in vision-based artificial intelligent systems, e.g., autonomous driving and robot navigation. Although previous work has shown its promising performance in semantic segmentation of images and videos, it is still quite challenging to anticipate future scene parsing with limited annotated training data. In this paper, we propose a novel model called STC-GAN, Spatio-Temporally Coupled Generative Adversarial Networks for predictive scene parsing, which employ both convolutional neural networks and convolutional long short-term memory (LSTM) in the encoderdecoder architecture. By virtue of STC-GAN, both spatial layout and semantic context can be captured by the spatial encoder effectively, while motion dynamics are extracted by the temporal encoder accurately. Furthermore, a coupled architecture is presented for establishing joint adversarial training where the weights are shared and features are transformed in an adaptive fashion between the future frame generation model and predictive scene parsing model. Consequently, the proposed STCGAN is able to learn valuable features from unlabeled video data. We evaluate our proposed STC-GAN on two public datasets, i.e., Cityscapes and CamVid. Experimental results demonstrate that our method outperforms the state-of-the-art.
Comparative study for 8 computational intelligence algorithms for human identification

The biometric system includes the algorithms, procedures, and devices which are utilized for the purpose of recognizing individuals according to their behavioral and physiological features. The approaches of Computational Intelligence (CI) are utilized extensively to establish biometric-based identities as well as overcoming non-idealities usually exist in samples. The objective of this paper is to analyze and evaluate the various computational intelligence (CI) approaches for the human identification based on biometrics . The study includes 8 top CI algorithms, namely; k-Nearest Neighbor(K-NN), Artificial Neural Networks (ANNs), Support vector machines (SVMs), Fuzzy Discernibility Matrix (FDM), Naïve Bayes (NB), k-means, Decision Trees (DTs), and Genetic algorithms (GAs). Also the study provides the technical characteristics and features of these algorithms as well as finds advantages and disadvantages of these methods . The analyzed algorithms can be selected according to quantity and quality of data presented at work.
Depth Selection for Deep ReLU Nets in Feature Extraction and Generalization

Deep learning is recognized to be capable of discovering deep features for representation learning and pattern recognition without requiring elegant feature engineering techniques by taking advantages of human ingenuity and prior knowledge. Thus it has triggered enormous research activities in machine learning and pattern recognition. One of the most important challenges of deep learning is to figure out relations between a feature and the depth of deep neural networks (deep nets for short) to reflect the necessity of depth. Our purpose is to quantify this feature-depth correspondence in feature extraction and generalization. We present the adaptivity of features to depths and vice-verse via showing a depth-parameter trade-off in extracting both single feature and composite features. Based on these results, we prove that implementing the classical empirical risk minimization on deep nets can achieve the optimal generalization performance for numerous learning tasks. Our theoretical results are verified by a series of numerical experiments including toy simulations and a real application of earthquake seismic intensity prediction.
Designing Distributed Fuzzy Rule-Based Models

In the design of fuzzy rule-based models, in the presence of high-dimensional data, we are faced with conceptual and algorithmic challenges. Conceptually, as the dimensionality of data increases, the notion of distance starts to be questionable, resulting in the well-known concentration effect. This has a direct detrimental effect given the fact that the distance is used in fuzzy clustering, using which the condition parts of the rules are being formed. Computationally, with the increase of dimensionality, the computing overhead becomes significant and has to be carefully addressed. In this article, we advocate a construction of distributed fuzzy rule-based models, where instead of a single monolithic (multivariable) rule-based model, we construct a collection of low-dimensional (in particular, 1- or 2-D) rule-based models and aggregate their results through some linear transformations. A suite of experimental studies realized using publicly available data are reported along with a comparative analysis engaging accuracy criteria and computing overhead. Interestingly, building distributed models exhibits some tangible benefits over the construction of the monolithic rule-based models. In terms of accuracy and computing costs, the gains of 1-D rule-based models with optimal linkage matrix (on average) are around 43.46% and 98.85%, respectively.
Learned 3D Shape Representations Using Fused Geometrically Augmented Images: Application to Facial Expression and Action Unit Detection

In this paper, we propose an approach to learn generic multi-modal mesh surface representations using a novel scheme for fusing texture and geometric data. Our approach defines an inverse mapping between different geometric descriptors computed on the mesh surface or its down-sampled version, and the corresponding 2D texture image of the mesh, allowing the construction of fused geometrically augmented images (FGAI). This new fused modality enables us to learn feature representations from 3D data in a highly efficient manner by simply employing standard CNNs in a transfer-learning mode. The proposed approach is both computationally and memory efficient, preserves intrinsic geometric information and learns highly discriminative feature representations by effectively fusing shape and texture information at data level. The efficacy of our approach is demonstrated for the tasks of facial action unit detection and expression classification. The extensive experiments conducted on the Bosphorus and BU-4DFE datasets show that our method produces a significant boost in the performance when compared to state-of-the-art solutions.
Application and techniques of opinion mining

The study and analysis of human behavior using a computer modeling approach is known as opinion mining or sentiment analysis. Data mining, Web extraction, text mining, etc. are the key areas of opinion mining. Social media platforms are gaining popularity and are becoming essential components of most people's lives. Various social networking websites, like Facebook, Twitter, and WhatsApp, are generating a huge amount of data and the mining of these data helps in discovering hidden and useful information with high potential. The calculation and evaluation of average inclinations to any opinion/sentiment toward any entity helps both the organization and the individual to get the right opinion about the ongoing trends or unfamiliar things. Various computational intelligence techniques are also used to analyze the sentiments of users. In this chapter the authors cover the fundamental concepts of opinion mining and sentiment analysis. The chapter also includes various techniques of opinion mining, along with various tools used to analyze opinions. Some key areas related to feature extraction, ontologies, and deep learning have also been discussed. Toward the end of the chapter research and future directions, along with references, have been given for further study.
Disappearing acts: Content moderation and emergent practices to preserve at-risk human rights–related content

Human rights groups, journalists, and “open source investigators” increasingly depend on social media platforms to collect eyewitness media documenting possible human rights violations and conflicts. And yet, this content—often graphic, controversial, even uploaded by perpetrators—is often removed by the platforms, for various reasons. This article draws on in-depth interviews to examine how practitioners reliant on human rights–related content understand, experience, and deal with platform content moderation and removals in their day-to-day work. Interviews highlighted that both the actual and anticipated removal of social media content complicated and added to practitioners’ work. In addition, practitioners unevenly possess the technical, financial, and organizational resources to mitigate the risks and ramifications of removal by preserving content and appealing content moderation decisions. This article sheds light on the impacts of content moderation for stakeholders other than the primary account holders, and highlights platforms’ affordances and shortcomings as archives of war.
Graph Representations for Programmable Photonic Circuits

We propose graph representations for reconfigurable photonic mesh circuits. Waveguide mesh circuits are abstracted into a graph to highlight the connectivity and topology. We model the optical ports as graph nodes. Performance metrics for each connection are incorporated into the edge attributes in categories such as propagation loss, crosstalk penalty, power consumption, phase accumulation, and so on. We use three types of graph representations for tunable couplers to model the flow of light and create a circuit graph representation to an example hexagonal mesh. The representation should respect the physics of waveguide circuits (e.g. directional flow of light). Of the three types, the directed graph with eight artificial nodes performs best for solving light distributions with feedback paths. This graph representation is demonstrated in four distribution cases: a single pair input-output, multi-pair inputs and outputs without collisions, a single input to multiple outputs (distribution), and multiple distributions without collisions. The programming tolerance against malfunctioning tunable elements is also demonstrated. With this circuit representation, we can reduce all these distribution cases to different graph problems and leverage a wealth of existing algorithms developed in graph theory to program the photonic mesh. Thus we provide a systematical strategy to design and program complex reconfigurable photonic circuits, especially in photonic meshes with feedback paths.
End-To-End Quantum Machine Learning Implemented with Controlled Quantum Dynamics

Toward quantum machine learning deployed on imperfect near-term intermediate-scale quantum (NISQ) processors, the entire physical implementation of should include as less as possible hand-designed modules with only a few ad-hoc parameters to be determined. This work presents such a hardware-friendly end-to-end quantum machine learning scheme that can be implemented with imperfect near-term intermediate-scale quantum (NISQ) processors. The proposal transforms the machine learning task to the optimization of controlled quantum dynamics, in which the learning model is parameterized by experimentally tunable control variables. Our design also enables automated feature selection by encoding the raw input to quantum states through agent control variables. Comparing with the gate-based parameterized quantum circuits, the proposed end-to-end quantum learning model is easy to implement as there are only few ad-hoc parameters to be determined. Numerical simulations on the benchmarking MNIST dataset demonstrate that the model can achieve high performance using only 3-5 qubits without downsizing the dataset, which shows great potential for accomplishing large-scale real-world learning tasks on NISQ processors.arning models. The scheme is promising for efficiently performing large-scale real-world learning tasks using intermediate-scale quantum processors.
A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm

Abstract The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.
A design review session protocol for the implementation of immersive virtual reality in usability-focused analysis

Research and applications related to Virtual Reality (VR) in the Architecture, Engineering, Construction and Facility Management (AEC/FM) industry are steadily increasing, being considered as one of the current trends in digital innovation. A convergence is currently on-going between Building Information Modelling (BIM), VR implementation and the soft landings principles, which highlight the need for a smooth transition from design to operation through the early involvement of clients and end-users. In particular, immersive virtual environments could allow stakeholders to experience the full-scale representation of a virtual facility prototype in an intuitive and engaging manner through immersion and sense of presence, promoting feedback collection during usability-focused design review meetings. On the other hand, despite the renewed interest in immersive VR, both technological and procedural challenges to its effective implementation still exist. The latter are within the scope of this study, which aims to address them in a systematic way as a comprehensive guideline for clients and design teams. This study describes the development of a VR-aided usability-focused design review session protocol for implementing immersive VR when clients and end-users are involved in design review meetings. It is the result of an inductive approach associated with qualitative research methods for data collection and data analysis. A case study has been selected as a main research method for facing the first step into the research problem. A further iteration of data collection and analysis was adopted to guarantee the validity of the research, including talks with experts and literature comparison. Finally, the session protocol has been developed in the form of a process map representing all the necessary phases and activities to consider for the effective adoption of immersive VR to evaluate design intents with clients and end-users. As a comprehensive summary, the session protocol fills a gap in the research on the adoption of virtual reality in the AEC industry, which was lacking a prescriptive and structured process to drive the effective use of this technology in collaborative meetings and decision-making processes.
Automatic recognition and location system for electric vehicle charging port in complex environment

This study proposes an automatic recognition and location system of electric vehicle charging port with application to automatic charging. The system obtains the charging port posture through image processing, and performs the insertion motion in combination with the robot arm to complete the charging gun insertion of the automatic charging link. The framework of the system is mainly divided into three parts, recognition, location and insertion. In the charging port recognition, the convolutional neural network-based method is used, and the recognition success rate is up to 98.9% under the light intensity of 4000 lux; in the location of the charging port, the method of solving the pose based on the circle feature is adopted. The average value of the position error is within 1.4 mm, and the average value of the attitude angle error is within 1.6°, which meets the accuracy requirement of the insertion experiment; in the charging gun insertion, the motion of the robot is planned by interpolation algorithm. The lower limit of the successful insertion is about 135 lux and the upper limit is about 9350 lux.
Sociomaterial movements of students' engagement in a school's makerspace

Abstract This study investigates the sociomaterial movements of student engagement in a school's makerspace. Here, we understand sociomaterial movements as emergent and relational, comprising complex dynamics of agency across students, teachers and materials in situated, culturally framed activities. Our study draws on data comprising 85 hours of video recordings of 9–12‐year‐old students' (N = 94) engagement in a technology‐rich makerspace in a Finnish elementary school. The video data were transcribed and analyzed qualitatively using a multimodal interaction analysis. The sociomaterial movements were found to be displayed across a tension‐laden continuum between (1) procedural activity—analysis and reflection; (2) individual activity—collaboration; (3) “doing school”—empowerment; and d) alienation—identification. Together, the study offers a potential approach for investigating and understanding the often overlooked workings of sociomateriality that constitutes students' emergent engagement and learning opportunities in science, technology, engineering and mathematics (STEAM) learning contexts.