Reviewerly/exHarmony huggingface dataset:
Peer-to-Peer Energy Trading through Swarm Intelligent Stackelberg Game

The development of smart grids has paved the way for sustainable energy infrastructure to transition towards decentralized energy trading. As intelligent agents, energy sources engage in energy trading based on their energy surplus or deficit. Buyers and sellers (participants) should achieve maximum payoffs in which buyers cut costs and sellers improve their utilities, and the security of sensitive information of smart agents must be guaranteed. This paper provides a blockchain-based energy trading network where intelligent agents can exchange energy in a secure manner, without the intervention of third parties. We model energy trading as a Stackelberg game, ensuring that the platform maximizes social welfare while participants increase their payoffs. Using the inherited characteristics of blockchain technology, a novel decentralized swarm intelligence technique is applied to solve the game while ensuring the privacy of the smart agents’ sensitive information. The numerical analysis demonstrates that the suggested method outperforms the present methods (Constant Utility Optimization, average method...) for optimizing the objectives of each agent by maximizing the sellers’ utilities and reducing the buyers’ costs. In addition, the experimental results demonstrate that it significantly reduces carbon footprint (15%) by enhancing energy exchange between intelligent agents.
Towards High-Performance Supersingular Isogeny Cryptographic Hardware Accelerator Design

Cryptosystems based on supersingular isogeny are a novel tool in post-quantum cryptography. One compelling characteristic is their concise keys and ciphertexts. However, the performance of supersingular isogeny computation is currently worse than that of other schemes. This is primarily due to the following factors. Firstly, the underlying field is a quadratic extension of the finite field, resulting in higher computational complexity. Secondly, the strategy for large-degree isogeny evaluation is complex and dependent on the elementary arithmetic units employed. Thirdly, adapting the same hardware to different parameters is challenging. Considering the evolution of similar curve-based cryptosystems, we believe proper algorithm optimization and hardware acceleration will reduce its speed overhead. This paper describes a high-performance and flexible hardware architecture that accelerates isogeny computation. Specifically, we optimize the design by creating a dedicated quadratic Montgomery multiplier and an efficient scheduling strategy that are suitable for supersingular isogeny. The multiplier operates on Fp2 under projective coordinate formulas, and the scheduling is tailored to it. By exploiting additional parallelism through replicated multipliers and concurrent isogeny subroutines, our 65 nm SMIC technology cryptographic accelerator can generate ephemeral public keys in 2.40 ms for Alice and 2.79 ms for Bob with a 751-bit prime setting. Sharing the secret key costs another 2.04 ms and 2.35 ms, respectively.
A machine learning low‐dropout regulator‐assisted differential power analysis attack countermeasure with voltage scaling

Summary False key‐controlled aggressive voltage scaling (FKCAVS) technique is a lightweight and effective leakage power analysis (LPA) attack countermeasure. However, the regular FKCAVS technique may not be utilized as a countermeasure against differential power analysis (DPA) attacks unconditionally. The primary reason is that the working frequency of DPA attacks is significantly higher than the corresponding frequency of LPA attacks. Thus, it is difficult to make the speed of voltage scaling keep pace with the speed of DPA attacks by employing the regular FKCAVS technique. In this paper, a fast FKCAVS technique is proposed to maximize the security of a cryptographic circuit (CC) against DPA attacks while minimizing the corresponding overhead by embedding a machine learning low‐dropout (LDO) regulator (MLLR). As shown in the result, by deploying the proposed FKCAVS technique, the measurement‐to‐disclose (MTD) value against DPA attacks is maintained over 1 million with less than 17.4% power/area overhead.
Neural 3D reconstruction from sparse views using geometric priors

Abstract Sparse view 3D reconstruction has attracted increasing attention with the development of neural implicit 3D representation. Existing methods usually only make use of 2D views, requiring a dense set of input views for accurate 3D reconstruction. In this paper, we show that accurate 3D reconstruction can be achieved by incorporating geometric priors into neural implicit 3D reconstruction. Our method adopts the signed distance function as the 3D representation, and learns a generalizable 3D surface reconstruction model from sparse views. Specifically, we build a more effective and sparse feature volume from the input views by using corresponding depth maps, which can be provided by depth sensors or directly predicted from the input views. We recover better geometric details by imposing both depth and surface normal constraints in addition to the color loss when training the neural implicit 3D representation. Experiments demonstrate that our method both outperforms state-of-the-art approaches, and achieves good generalizability.
Tor Anonymous Traffic Identification Based on Parallelizing Dilated Convolutional Network

The widespread use of the onion browser (Tor) has provided a breeding ground for the proliferation of cybercriminal activities and the Tor anonymous traffic identification method has been used to fingerprint anonymous web traffic and identify the websites visited by illegals. Despite the considerable progress in existing methods, problems still exist, such as high training resources required for the identification model, bias in fingerprint features due to the fast iteration of anonymous traffic and singularity in the definition of traffic direction features. On this basis, a Tor anonymous traffic identification model based on parallelizing dilated convolutions multi-feature analysis has been proposed in this paper in order to address these problems and perform better in website fingerprinting. A single-sample augmentation of the traffic data and a model combining multi-layer RBMs and parallelizing dilated convolutions are performed, and binary classification and multi-classification of websites are conducted for different scenarios. Our experiment shows that the proposed Tor anonymous traffic recognition method achieves 94.37% accuracy and gains a significant drop in training time in both closed-world and open-world scenarios. At the same time, the enhanced traffic data enhance the robustness and generalization of our model. With our techniques, our training efficiency has been improved and we are able to achieve the advantage of bi-directional deployability on the communication link.
Human Pose Estimation Based on a Spatial Temporal Graph Convolutional Network

To address the problem of poor detection and under-utilization of the spatial relationship between nodes in human pose estimation, a method based on an improved spatial temporal graph convolutional network (ST-GCN) model is proposed. Firstly, upsampling and segmented random sampling strategies are used to effectively solve the problems of class imbalance and the large sequence length of the dataset. Secondly, an improved detection transformer (DETR) structure is added to effectively suppress the generation of non-maximal suppression (NMS) and anchor points, a multi-head attention (M-ATT) module is introduced into each ST-GCN cell to capture richer feature information, and a residual module is introduced into the 9th ST-GCN cell to avoid possible network degradation in deep networks. In addition, strategies such as warmup, regularization, loss functions, and optimizers are configured to improve the model’s performance. The experimental results show that the average percentage of correct keypoints (PCK) of this method are 93.2% and 92.7% for the FSD and MPII datasets, respectively, which is 1.9% and 1.7% higher than the average PCK of the original ST-GCN method. Moreover, the confusion matrix corresponding to this method also indicated that the model has high recognition accuracy. In addition, comparison experiments with ST-GCN and other methods show that the computation of the model corresponding to this method is about 1.7 GFLOPs and the corresponding MACs are about 6.4 GMACs, which is a good performance.
Characterization of a Driven Two-Level Quantum System by Supervised Learning

We investigate the extent to which a two-level quantum system subjected to an external time-dependent drive can be characterized by supervised learning. We apply this approach to the case of bang-bang control and the estimation of the offset and the final distance to a given target state. For any control protocol, the goal is to find the mapping between the offset and the distance. This mapping is interpolated using a neural network. The estimate is global in the sense that no a priori knowledge is required on the relation to be determined. Different neural network algorithms are tested on a series of data sets. We show that the mapping can be reproduced with very high precision in the direct case when the offset is known, while obstacles appear in the indirect case starting from the distance to the target. We point out the limits of the estimation procedure with respect to the properties of the mapping to be interpolated. We discuss the physical relevance of the different results.
Knowledge vector representation of three-dimensional convex polyhedrons and reconstruction of medical images using knowledge vector

Three-dimensional image construction and reconstruction plays an important role in various applications of the real world in the field of computer vision. In the last three decades, researchers are continually working in this area because construction and reconstruction is an important approach in medical imaging. Reconstruction of the 3D image allows us to find the lesion information of the patients which could offer a new and accurate approach for the diagnosis of the disease and it adds a clinical value. Considering this, a novel approach is proposed for the construction and reconstruction of the image. First, a syntactic pattern recognition-based algorithm is implemented to extract the features from the 2D image. The proposed algorithm takes an input image and extracts the features from the image and these features (Knowledge vector) consist of direction code and length. In addition, a unique and novel algorithm is developed that can rebuild an image using a knowledge vector. Reconstruction allows us to investigate the interior details of 3D images, such as the object's size, form, and structure. The proposed algorithm performance is assessed on a medical imaging dataset, and the findings are outperformed. Performances of the proposed algorithms are evaluated on Kaggle brain MRI dataset and Medical MRI datasets which is collected from Pentagram research institute, Hyderabad. As per the experimental analysis, the proposed method gives 93.89% of accuracy on Kaggle brain MRI dataset and 97.24% on Medical MRI dataset which is better than exiting state of art methods.
Big Data and Metaverse Revolutionizing the Futuristic Fintech Industry

The Fintech industry is no stranger to transformation and disruption. With the emergence of new technologies such as Big Data and the Metaverse, finance companies are quickly leveraging these technologies to revolutionize the virtual fintech sector. Big data transforms how financial institutions gather, analyze, and utilize data. The sheer amount of data available has opened up countless opportunities for better customer understanding and insight into business operations. Companies can use this data to create predictive models that identify emerging trends in customer behavior, market activity, and other aspects of their business. By utilizing these insights, companies can make smarter decisions about products, services, marketing strategies, and more. The Metaverse is another technology with massive potential for disrupting the Fintech sector. The Metaverse enables businesses to offer virtual goods and services without physical stores or retail outlets. This makes it easier for companies to scale their operations across multiple regions and gives customers more options when choosing a financial service provider. Additionally, using digital currencies like Bitcoin helps reduce transaction costs significantly while increasing security compared to traditional payment methods like credit cards or PayPal.
Universal compilation for quantum state tomography

Universal compilation is a training process that compiles a trainable unitary into a target unitary. It has vast potential applications from depth-circuit compressing to device benchmarking and quantum error mitigation. Here we propose a universal compilation algorithm for quantum state tomography in low-depth quantum circuits. We apply the Fubini-Study distance as a trainable cost function and employ various gradient-based optimizations. We evaluate the performance of various trainable unitary topologies and the trainability of different optimizers for getting high efficiency and reveal the crucial role of the circuit depth in robust fidelity. The results are comparable with the shadow tomography method, a similar fashion in the field. Our work expresses the adequate capability of the universal compilation algorithm to maximize the efficiency in the quantum state tomography. Further, it promises applications in quantum metrology and sensing and is applicable in the near-term quantum computers for various quantum computing tasks.
Transferable Deep Metric Learning for Clustering

Clustering in high dimension spaces is a difficult task; the usual distance metrics may no longer be appropriate under the curse of dimensionality. Indeed, the choice of the metric is crucial, and it is highly dependent on the dataset characteristics. However a single metric could be used to correctly perform clustering on multiple datasets of different domains. We propose to do so, providing a framework for learning a transferable metric. We show that we can learn a metric on a labelled dataset, then apply it to cluster a different dataset, using an embedding space that characterises a desired clustering in the generic sense. We learn and test such metrics on several datasets of variable complexity (synthetic, MNIST, SVHN, omniglot) and achieve results competitive with the state-of-the-art while using only a small number of labelled training datasets and shallow networks.
Reliability and Validity of the Turkish Version of the Teachers’ Basic ICT Competence Beliefs Scale

The present study seeks to adapt the Teachers’ Basic Information Communication Technology (ICT) Competence Beliefs Scale, developed by Rubach and Lazarides (2021), into Turkish and test the adapted scale's validity and reliability. The initial step involved conducting a linguistic equivalence of the scale from English to Turkish with 62 English language teachers in a pre-test. Subsequently, the Turkish version of the scale was administered to 356 teachers (69.7% female, 30.3% male) in Turkey to assess its validity and reliability. Participating teachers were from different subjects (e.g., 9.8% science, 7.9% mathematics, 3.7% social science) and school types (27.5% primary school, 55.3% secondary school, 17.1% others). Results of confirmatory factor analysis indicated the original six-factor structure with three first-order and three second-order factors that best fitted the data. The same competence dimensions were indicated in the Turkish contexts as in the original instrument, i.e., information and data literacy; communication and collaboration; digital content creation; safety and security; problem-solving; analyzing and reflecting. The correlations between all six first-order factors were between .58 ≥ r ≥.79. All factors showed good reliability indices, i.e., α &amp;gt; .83, ω &amp;gt; .83 and CR &amp;gt; .72. The adapted instrument was found to be invariant across gender. Mean-level differences among gender groups point to one difference with male teachers reporting higher competence beliefs for digital content creation compared to female teachers. In conclusion, the results of this replication study support the cross-cultural transferability of the original Teachers' Basic ICT Competence Beliefs instrument developed by Rubach and Lazarides (2019).
Self-supervised learning of scene flow with occlusion handling through feature masking

In this work, we improve the optical flow and depth based on the important observation that they should share the geometric structure of the reference image. We initially propose a feature masking method to reduce the occlusion impact on the optical flow and depth by producing preliminary motion features that share the structure of the reference image. In addition, we propose a deformable decoder that learns the geometrical structure of the reference image in the form of a group of offsets and uses them to adapt the motion features of flow and depth maps, thereby preventing incorrect propagation in occluded regions and providing more structural details in the other regions. Furthermore, we recursively update the optical flow with self-supervised cues learned from the rigid flow and optical flow. Our method achieves a new state-of-the-art result for the optical flow on the KITTI 2015 benchmark with F1 = 11.17%.
Using multimodal educational apps to increase the vocabulary of children with and without reading difficulties

This study examined the effects of a multimodal vocabulary instruction app on vocabulary learning of first-grade students. The effects of listening to a recorded word explanation was also explored Employing a within-subject design, 69 students practiced 10 novel words on a multimodal vocabulary iPad app. Their performances were compared to another ten words with recorded word explanation and to ten unexplained control words. As a whole group, students learned more word meanings when the additional activity of listening to the recorded word explanation was preceded than when students practiced the new words solely on the vocabulary app. When the students were divided into less-skilled and more-skilled readers, unlike the less-skilled readers who mimicked the whole group’s learning pattern, more-skilled readers learned significantly more word meanings only when students listened to the recorded word meanings and practiced these words on the vocabulary app. This research suggests that regardless of the present reading levels, young readers may benefit the most when a vocabulary app is frontloaded with target word explanations. This study contributes to our understanding of how multimodal app technology can benefit students’ vocabulary learning with varied reading abilities.
A More Robust Model to Answer Noisy Questions in KBQA

In practical applications, the raw input to a Knowledge Based Question Answering (KBQA) system may vary in forms, expressions, sources, etc.As a result, the actual input to the system may contain various errors caused by various noise in raw data and processes of transmission, transformation, translation, etc.As a result, it is significant to evaluate and enhance the robustness of a KBQA model to various noisy questions.In this paper, we generate 29 datasets of various noisy questions based on the original SimpleQuestions dataset to evaluate and enhance the robustness of a KBQA model, and propose a model which is more robust to various noisy questions.Compared with traditional methods, the main contribution in this paper is that we propose a method of generating datasets of different noisy questions to evaluate the robustness of a KBQA model, and propose a KBQA model which contains incremental learning and Mask Language Model (MLM) in the question answering process, so that our model is less affected by different kinds of noise in questions and achieves higher accuracies on datasets of different noisy questions, which shows its robustness.Experimental results show that our model achieves an average accuracy of 78.1% on these datasets and outperforms the baseline BERT-based model by an average margin of 5.0% with the similar training cost.In addition, further experiments show that our model is compatible with other pre-trained models such as ALBERT and ELECTRA.
A Survey on Deep Learning for Website Fingerprinting Attacks and Defenses

The attacks and defenses on the information of which website pages are visited by users are important research subjects in the field of privacy enhancing technologies, they are termed as website fingerprinting (WF) attacks and defenses. Nowadays, deep learning is an important tool in many research areas, including WF attacks and defenses. In this paper, we offer a comprehensive survey on deep learning for WF attacks and defenses. After a brief introduction, we first summarize deep learning, WF attacks, and WF defenses. For deep learning, we review the common paradigms, architectures, and performance metrics. For WF attacks, we review the approaches, challenges and solutions. The approaches include deep learning, traditional machine learning, and other methods. Challenges and solutions cover multi-tab browsing, concept drift, and the base rate fallacy. For WF defenses, we review the strategies and approaches. Then, we survey deep learning for WF attacks, and deep learning for WF defenses. In deep learning for WF attacks, we survey in detail the deep learning paradigms, architectures of WF attack models, and the performance of several representative WF attack models, and look into the future. In deep learning for WF defenses, we survey the architecture, efficacy and overhead of deep learning models in WF defenses, and look into the future. In the end, we summarize this paper.
Image Stitching Method of Bone Stick Fragment Based on Similarity Freeman Code Matching

A large number of bone stick fragments were unearthed at the Weiyang Palace, and the manual stitching method is inefficient and time-consuming, therefore Image Stitching Method of Bone Stick Fragment Based on Similarity Freeman code Matching is proposed by this paper to improve bone stick stitching efficiency.The Freeman code is used to represent the contour of the true color RGB image of bone stick fragments, and the direction change dense feature of the bone stick fragment fracture is fused with the Freeman code difference to represent the change direction feature to locate the fracture boundary.At the same time, the integer basic operation is used to replace the complex calculus calculation to improve the efficiency of the algorithm; The longest common subsequence algorithm is used to perform local similarity rough matching on Freeman code of the contour at the fragment fracture, and then the angle and distance error threshold is set according to the prior knowledge of manual stitching to reduce the matching error; Finally, according to the matching results, the complete bone stick contour binary image is drawn.Compared with other algorithms, the contour Cosine similarity accuracy of the bone stick image stitched by this algorithm is improved by 21.68%, which can be accurate to 97.80%; the Editing distance similarity accuracy is improved by 14.29%, which can be accurate to 91.87%;the Peak signal-to-noise ratio is improved by 83%, which can be accurate to 31.13%;theStructural Similarity is improved by 29%, which can be accurate to 0.87; the Figure of merit is improved by 39%, which can be accurate to 0.85.The experiment results show that the algorithm proposed in this paper can effectively assist the stitching work of bone stick cultural relics.