Reviewerly/exHarmony huggingface dataset:
A Lightweight Model-Based Evolutionary Consensus Protocol in Blockchain as a Service for IoT

Internet of Things (IoT) is experiencing fast proliferation with emerging trends in autonomy and local decision-making to avoid the explosive burden on network infrastructure between cloud and edge. Thereby, blockchain as a Service (BaaS) for IoT, as an emerging distributed services computing paradigm, has drawn intense attention due to its decentralization, auditability, and tamper-resistance. However, the primary challenge is to design a tailor-made consensus protocol that is applicable to BaaS for IoT. Existing consensus protocols generally focus on power-intensive environments, which is not feasible for power-constrained BaaS-enabled IoT systems. In this article, to fully exploit BaaS's superiority (e.g., to sharing data securely), we propose a lightweight model-based evolutionary consensus protocol called Proof of Evolutionary Model (PoEM) that can improve the quality of BaaS in IoT environments. Beyond existing rule-based consensus protocols, PoEM iteratively trains a machine learning model to achieve consensus. In this way, PoEM enhances consensus efficiency and enables low-performance IoT devices to be involved. Moreover, considering IoT environments’ dynamics, a novel mechanism is designed to manage nodes joining and exiting dynamically. Extensive analytical and experimental results show PoEM's improved consensus efficiency and applicability in dynamic BaaS-based IoT environments while providing high-level security guarantees.
Benchmarking deep learning approaches for all-vs-all conjunction screening

The all-vs-all problem, for which conjunctions are screened for over all possible sets of catalogued objects, is crucial for space traffic management and space situational awareness, but is a computational challenge owing to the vast and growing number of possible conjunction pairs. In this work, we present the application of deep learning techniques to this problem, framing conjunction screening as a machine learning classification task. We investigate the performance of different input data representations and model architectures on a realistic all-vs-all dataset, generated using the CNES BAS3E space surveillance simulation framework, and consisting of 170 million object pairs over a 7-day screening period. These approaches are benchmarked against operationally used classical filters in both screening capability and computational efficiency, and the ability of deep learning algorithms to cope and aid with the scales required for current and future operational all-vs-all scenarios is demonstrated.
Developing NLP Tool for Linguistic Analysis of Turkic Languages

Today we see the active development of natural language processing technologies, including the morphological analysis of word forms. In this context, the development of more universal (general) algorithms that we can work with several languages at once is of particular relevance. As a result, algorithms that can reliably process texts of one family will be much more popular than those that can work with only one language. In our work, we presented a flexible tool for working with Turkic languages, in particular, for the morphological analysis of words. As is known, the Turkic-speaking languages belong to agglutinative languages, which, in turn, are formed by the concatenation of morphemes. It follows from this that by means of various combinations of affixes it is possible to construct a huge number of words in each of the languages of this family. Our tool allowed to carry out morphological analysis for almost all Turkic languages.
The Abe-Okamoto Partially Blind Signature Scheme Revisited

Partially blind signatures, an extension of ordinary blind signatures, are a primitive with wide applications in e-cash and electronic voting. One of the most efficient schemes to date is the one by Abe and Okamoto (CRYPTO 2000), whose underlying idea—the OR-proof technique—has served as the basis for several works. We point out several subtle flaws in the original proof of security, and provide a new detailed and rigorous proof, achieving similar bounds as the original work. We believe our insights on the proof strategy will find useful in the security analyses of other OR-proof-based schemes.
SwiftEC: Shallue-van de Woestijne Indifferentiable Function to Elliptic Curves

Hashing arbitrary values to points on an elliptic curve is a required step in many cryptographic constructions, and a number of techniques have been proposed to do so over the years. One of the first ones was due to Shallue and van de Woestijne (ANTS-VII), and it had the interesting property of applying to essentially all elliptic curves over finite fields. It did not, however, have the desirable property of being indifferentiable from a random oracle when composed with a random oracle to the base field. Various approaches have since been considered to overcome this limitation, starting with the foundational work of Brier et al. (CRYPTO 2011). For example, if $$f:\mathbb F_{q}\rightarrow E(\mathbb F_{q})$$ is the Shallue–van de Woestijne (SW) map and $$\mathfrak {h}_1,\mathfrak {h}_2$$ are two independent random oracles to $$\mathbb F_{q}$$ , we now know that $$m\mapsto f\big (\mathfrak {h}_1(m)\big )+f\big (\mathfrak {h}_2(m)\big )$$ is indifferentiable from a random oracle. Unfortunately, this approach has the drawback of being twice as expensive to compute than the straightforward, but not indifferentiable, $$m\mapsto f\big (\mathfrak {h}_1(m)\big )$$ . Most other solutions so far have had the same issue: they are at least as costly as two base field exponentiations, whereas plain encoding maps like f cost only one exponentiation. Recently, Koshelev (DCC 2022) provided the first construction of indifferentiable hashing at the cost of one exponentiation, but only for a very specific class of curves (some of those with j-invariant 0), and using techniques that are unlikely to apply more broadly. In this work, we revisit this long-standing open problem, and observe that the SW map actually fits in a one-parameter family $$(f_u)_{u\in \mathbb F_{q}}$$ of encodings, such that for independent random oracles $$\mathfrak {h}_1, \mathfrak {h}_2$$ to $$\mathbb F_{q}$$ , $$F:m\mapsto f_{\mathfrak {h}_2(m)}\big (\mathfrak {h}_1(m)\big )$$ is indifferentiable. Moreover, on a very large class of curves (essentially those that are either of odd order or of order divisible by 4), the one-parameter family admits a rational parametrization, which lets us compute F at almost the same cost as small f, and finally achieve indifferentiable hashing to most curves with a single exponentiation. Our new approach also yields an improved variant of the Elligator Squared technique of Tibouchi (FC 2014) that represents points of arbitrary elliptic curves as close-to-uniform random strings.
Full Quantum Equivalence of Group Action DLog and CDH, and More

Cryptographic group actions are a relaxation of standard cryptographic groups that have less structure. This lack of structure allows them to be plausibly quantum resistant despite Shor's algorithm, while still having a number of applications. The most famous example of group actions are built from isogenies on elliptic curves. Our main result is that CDH for abelian group actions is quantumly equivalent to discrete log. Galbraith et al. (Mathematical Cryptology) previously showed perfectly solving CDH to be equivalent to discrete log quantumly; our result works for any non-negligible advantage. We also explore several other questions about group action and isogeny protocols.
Multi-layer security based multiple image encryption technique

This paper designs a novel multiple image encryption technique utilizing affine Hill cipher (AHC), reality-preserving two-dimensional discrete fractional Hartley transform (RP2DFrHT) and generalized two-dimensional (2D) Arnold map (AM). In our technique, three indexed images are obtained from three color images. Afterwards, AHC performs a strong degree of confusion and diffusion operations and RP2DFrHT provides real domain output image. Lastly, 2D AM dislocates the image pixel position. After the encryption process, single-channel real-valued encrypted image is obtained which gives convenience in the display, storage and transmission of an image over an unsecured network. The proposed technique is multi-layer secured in the time, frequency and co-ordinate domains. Moreover, the secret keys, arrangements and positions of used parameters all are imperative for the correct decryption process of our technique. Simulation results, security analysis, statistical analysis and comparison analysis ensure that the proposed technique has an excellent encryption effect, sufficient security and robustness.
Influence of Avatar Facial Appearance on Users’ Perceived Embodiment and Presence in Immersive Virtual Reality

Immersive virtual reality (VR) based on head-mounted displays has been identified as one of the key interaction technologies of the future metaverse, which comprises diverse interconnected virtual worlds and users who traverse between those worlds and interact with each other. Interaction in immersive VR entails the use of avatars that represent users. Previous research has shown that avatar appearance (e.g., body type, body visibility, and realism) affects the senses of embodiment and presence, which are among the key indicators of successful immersive VR. However, research on how the similarity between an avatar’s face and the user’s face affects embodiment and presence is lacking. We conducted a mixed-method experiment with 23 young adults (10 males, 13 females, mean age: 25.22) involving a VR scene with rich embodiment, a virtual mirror, and interaction with a virtual character. The participants were assigned to two groups: Group 1 had avatars based on their own faces, and Group 2 had avatars based on a stranger’s face. The results indicated that Group 1 experienced higher embodiment with no significant differences in presence scores. Additionally, we identified moderate and significant correlations between presence and embodiment, including their subscales. We conclude that the realism and similarity in an avatar’s appearance is important for embodiment, and that both embodiment and presence are intertwined factors contributing to immersive VR user experience.
Visualization Technology and Deep-Learning for Multilingual Spam Message Detection

Spam detection is an essential and unavoidable problem in today’s society. Most of the existing studies have used string-based detection methods with models and have been conducted on a single language, especially with English datasets. However, in the current global society, research on languages other than English is needed. String-based spam detection methods perform different preprocessing steps depending on language type due to differences in grammatical characteristics. Therefore, our study proposes a text-processing method and a string-imaging method. The CNN 2D visualization technology used in this paper can be applied to datasets of various languages by processing the data as images, so they can be equally applied to languages other than English. In this study, English and Korean spam data were used. As a result of this study, the string-based detection models of RNN, LSTM, and CNN 1D showed average accuracies of 0.9871, 0.9906, and 0.9912, respectively. On the other hand, the CNN 2D image-based detection model was confirmed to have an average accuracy of 0.9957. Through this study, we present a solution that shows that image-based processing is more effective than string-based processing for string data and that multilingual processing is possible based on the CNN 2D model.
Evaluation of Portable X-ray Fluorescence Analysis and Its Applicability As a Tool in Geochemical Exploration

Large-scale, high-density geochemical explorations entail enormous workloads and high costs for sample analysis, but, for early mineral exploration, absolute concentrations are not essential. Geochemists require ranges, dynamics of variation, and correlations for early explorations rather than absolute accuracy. Thus, higher work efficiency and lower costs for sample analysis are desirable for geochemical exploration. This study comprehensively analyzed the reliability and applicability of portable X-ray fluorescence (pXRF) spectrometry in geochemical exploration. The results show that pXRF can be applied effectively to rock and rock powder samples, and sample preparation and a longer detection time have been shown to increase the precision of the pXRF results. When pXRF is used on rock samples, if less than 30% of the samples are assessed as containing an element, the element is usually undetectable using pXRF when these rock samples are prepared as rock powders, indicating that the data about the detected element are unreliable; thus, it is suggested that some representative samples should be selected for testing before starting to use a pXRF in a geochemical exploration project. In addition, although the extended detection time increased the reliability of the analysis results, an increase in detection time of more than 80 s did not significantly affect the accuracy of the results. For this reason, the recommended detection time for the pXRF analysis of rock powder samples is 80 s for this study. pXRF has the advantages of being low-cost, highly efficient, and stable, and its results are reliable enough to exhibit the spatial distribution of indicator elements (arsenic, nickel, lead, sulfur, titanium, and zinc) in polymetallic mineralization exploration. Therefore, pXRF is recommendable for practical use in geochemical exploration.
Time-Varying Pseudoinversion Based on Full-Rank Decomposition and Zeroing Neural Networks

The computation of the time-varying matrix pseudoinverse has become crucial in recent years for solving time-varying problems in engineering and science domains. This paper investigates the issue of calculating the time-varying pseudoinverse based on full-rank decomposition (FRD) using the zeroing neural network (ZNN) method, which is currently considered to be a cutting edge method for calculating the time-varying matrix pseudoinverse. As a consequence, for the first time in the literature, a new ZNN model called ZNNFRDP is introduced for time-varying pseudoinversion and it is based on FRD. Five numerical experiments investigate and confirm that the ZNNFRDP model performs as well as, if not better than, other well-performing ZNN models in the calculation of the time-varying pseudoinverse. Additionally, theoretical analysis and numerical findings have both supported the effectiveness of the proposed model.
IoT and Blockchain Integration: Applications, Opportunities, and Challenges

During the recent decade, two variants of evolving computing networks have augmented the Internet: (i) The Internet of Things (IoT) and (ii) Blockchain Network(s) (BCNs). The IoT is a network of heterogeneous digital devices embedded with sensors and software for various automation and monitoring purposes. A Blockchain Network is a broadcast network of computing nodes provisioned for validating digital transactions and recording the “well-formed” transactions in a unique data storage called a blockchain ledger. The power of a blockchain network is that (ideally) every node maintains its own copy of the ledger and takes part in validating the transactions. Integrating IoT and BCNs brings promising applications in many areas, including education, health, finance, agriculture, industry, and the environment. However, the complex, dynamic and heterogeneous computing and communication needs of IoT technologies, optionally integrated by blockchain technologies (if mandated), draw several challenges on scaling, interoperability, and security goals. In recent years, numerous models integrating IoT with blockchain networks have been proposed, tested, and deployed for businesses. Numerous studies are underway to uncover the applications of IoT and Blockchain technology. However, a close look reveals that very few applications successfully cater to the security needs of an enterprise. Needless to say, it makes less sense to integrate blockchain technology with an existing IoT that can serve the security need of an enterprise. In this article, we investigate several frameworks for IoT operations, the applicability of integrating them with blockchain technology, and due security considerations that the security personnel must make during the deployment and operations of IoT and BCN. Furthermore, we discuss the underlying security concerns and recommendations for blockchain-integrated IoT networks.
Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization

Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems. Saliency maps are frequently used to provide interpretability for deep neural networks. However, in the RL domain, existing saliency map approaches are either computationally expensive and thus cannot satisfy the real-time requirement of real-world scenarios or cannot produce interpretable saliency maps for RL policies. In this work, we propose an approach of Distillation with selective Input Gradient Regularization (DIGR) which uses policy distillation and input gradient regularization to produce new policies that achieve both high interpretability and computation efficiency in generating saliency maps. Our approach is also found to improve the robustness of RL policies to multiple adversarial attacks. We conduct experiments on three tasks, MiniGrid (Fetch Object), Atari (Breakout) and CARLA Autonomous Driving, to demonstrate the importance and effectiveness of our approach.
MRE-Net: Multi-Rate Excitation Network for Deepfake Video Detection

The current social media is flooded with hyper realistic face-synthetic videos due to the explosion of DeepFake technology that has brought a serious impact on human society security, which calls for further exploring on deepfake video detection methods. Existing methods attempt to isolated capture spatial artifacts or extract the homogeneous temporal inconsistency to detect deepfake video, but little attention has been paid to the exploitation of dynamic spatial-temporal inconsistency. To mitigate this issue, in this paper, we propose a novel Multi-Rate Excitation Network (MRE-Net) to effectively excite dynamic spatial-temporal inconsistency from the perspective of multiple rates for deepfake video detection. The proposed MRE-Net is composed of two components: Bipartite Group Sampling (BGS) and multiple rate branches. The BGS draws the entire video into multiple bipartite groups with different rates to cover various face motion dynamic evolution. We further design multiple rate branches to capture both short-term and long-term spatial-temporal inconsistency from corresponding bipartite groups of BGS. Concretely, for the early stages of the multi-rate branches, Momentary Inconsistency Excitation (MIE) module is developed to encode the spatial artifacts and intra-group short-term temporal inconsistency. Meanwhile, for the last stages of the multi-rate branches, Longstanding Inconsistency Excitation (LIE) module is constructed to perceive inter-group long-term temporal dynamics. Extensive experiments and visualizations conducted on four popular datasets demonstrate the effectiveness of the proposed method against state-of-the-art deepfake detection methods.
Case-based similar image retrieval for weakly annotated large histopathological images of malignant lymphoma using deep metric learning

In the present study, we propose a novel case-based similar image retrieval (SIR) method for hematoxylin and eosin (H&E) stained histopathological images of malignant lymphoma. When a whole slide image (WSI) is used as an input query, it is desirable to be able to retrieve similar cases by focusing on image patches in pathologically important regions such as tumor cells. To address this problem, we employ attention-based multiple instance learning, which enables us to focus on tumor-specific regions when the similarity between cases is computed. Moreover, we employ contrastive distance metric learning to incorporate immunohistochemical (IHC) staining patterns as useful supervised information for defining appropriate similarity between heterogeneous malignant lymphoma cases. In the experiment with 249 malignant lymphoma patients, we confirmed that the proposed method exhibited higher evaluation measures than the baseline case-based SIR methods. Furthermore, the subjective evaluation by pathologists revealed that our similarity measure using IHC staining patterns is appropriate for representing the similarity of H&E stained tissue images for malignant lymphoma.
Relational-Convergent Transformer for image captioning

Image captioning describes the visual content of a given image by using natural language sentences, and plays a key role in the fusion and utilization of the image features. However, in the existing image captioning models, the decoder sometimes fails to efficiently capture the relationships between image features because of their lack of sequential dependencies. In this paper, we propose a Relational-Convergent Transformer (RCT) network to obtain complex intramodality representations in image captioning. In RCT, a Relational Fusion Module (RFM) is designed for capturing the local and global information of an image by a recursive fusion. Then, a Relational-Convergent Attention (RCA) is proposed, which is composed of a self-attention and a hierarchical fusion module for aggregating global relational information to extract a more comprehensive intramodal contextual representation. To validate the effectiveness of the proposed model, extensive experiments are conducted on the MSCOCO dataset. The experimental results show that the proposed method outperforms some of the state-of-the-art methods.
How Does Neural Network Model Capacity Affect Photovoltaic Power Prediction? A Study Case

The use of models capable of forecasting the production of photovoltaic (PV) energy is essential to guarantee the best possible integration of this energy source into traditional distribution grids. Long Short-Term Memory networks (LSTMs) are commonly used for this purpose, but their use may not be the better option due to their great computational complexity and slower inference and training time. Thus, in this work, we seek to evaluate the use of neural networks MLPs (Multilayer Perceptron), Recurrent Neural Networks (RNNs), and LSTMs, for the forecast of 5 min of photovoltaic energy production. Each iteration of the predictions uses the last 120 min of data collected from the PV system (power, irradiation, and PV cell temperature), measured from 2019 to mid-2022 in Maceió (Brazil). In addition, Bayesian hyperparameters optimization was used to obtain the best of each model and compare them on an equal footing. Results showed that the MLP performs satisfactorily, requiring much less time to train and forecast, indicating that they can be a better option when dealing with a very short-term forecast in specific contexts, for example, in systems with little computational resources.