Reviewerly/exHarmony huggingface dataset:
Securing Federated Learning Based on Blockchain Technology

A blockchain-based federated learning approach with secure aggregation in a trusted execution environment for the Internet of Things (IoT). The proposed approach aims to address the privacy and security concerns associated with federated learning in IoT environments. The approach involves using a blockchain to store the learning model and to maintain a distributed ledger of transactions. The learning model is trained on local IoT devices using federated learning techniques, with each device contributing its local data. The aggregation of the model updates is performed securely within a trusted execution environment, using homomorphism encryption and secret sharing techniques. The proposed approach offers several advantages over traditional federated learning approaches, including improved privacy and security, increased scalability, and enhanced trustworthiness. It also enables the creation of a decentralized and democratic learning environment, where each device has an equal say in the learning process. The approach is evaluated using a real-world dataset, and the results demonstrate its effectiveness in terms of accuracy and privacy preservation. The paper concludes that the proposed approach has the potential to enable secure and scalable federated learning in IoT environments, with applications in healthcare, smart cities, and other domains. This paper offers a blockchain-based federated learning (FL) framework with an Intel Software Guard Extension (SGX)-based trusted execution environment (TEE) for safely aggregating local models in the Industrial Internet of Things (IoT) Local models in FL can be modified with by attackers. As a result, a global model derived from manipulated local models may be incorrect. As a result, the proposed system makes use of a blockchain network to secure model aggregation. Each blockchain node contains an SGX-enabled CPU that secures the FL-based aggregating processes required to construct a global model. Blockchain nodes may validate the aggregated model's validity, perform a blockchain consensus method to secure the model's integrity, and add it to the distributed ledger for tamper-proof storage. . Before utilising the aggregated model, each cluster can acquire it from the blockchain and validate its fidelity. To assess the performance of the proposed system, we ran many experiments using various CNN models and datasets
Lexical modeling for the development of Amharic automatic speech recognition systems

Amharic is the second most spoken Semitic language after Arabic. It has its own syllabary writing system, each character representing a consonant and a vowel. Automatic Speech Recognition (ASR) researches for Amharic have been conducted on the basis of grapheme-based pronunciation lexicon, taking advantage of the nature of its writing system. However, the epenthetic vowel and the glottal stop consonant represented in the writing system may not be pronounced in all of their occurrences. Moreover, the writing system does not differentiate geminated and non-geminated forms of consonants. Therefore, the grapheme-based pronunciation lexicon used so far has limitations with regard to these language features. To handle these limitations, we have prepared word- and morpheme-based pronunciation lexicons using data-driven and knowledge-driven experts’ transcription. The data-driven transcription has been used for the preparation of training pronunciation lexicon while the knowledge-driven has been used to prepare morpheme- and word-based pronunciation lexicons for decoding. When morpheme-based knowledge-driven lexicons are used, better ASR performance (compared with the baseline ASR system that used grapheme-based lexicon) has been achieved although the number of phones is much more (60) than the number of phones used in the grapheme-based lexicon (37).
Multi-scale semantic enhancement network for object detection

In the field of object detection, feature pyramid network (FPN) can effectively extract multi-scale information. However, the majority of FPN-based methods suffer from a semantic gap between features of various sizes before feature fusion, which can lead to feature maps with significant aliasing. In this paper, we present a novel multi-scale semantic enhancement feature pyramid network (MSE-FPN) which consists of three effective modules: semantic enhancement module, semantic injection module, and gated channel guidance module to alleviate these problems. Specifically, inspired by the strong ability of the self-attention mechanism to model context, we propose a semantic enhancement module to model global context to obtain the global semantic information before feature fusion. Then we propose the semantic injection module to divide and merge global semantic information into feature maps at various scales to narrow the semantic gap between features at different scales and efficiently utilize the semantic information of high-level features. Finally, to mitigate feature aliasing caused by feature fusion, the gated channel guidance module selectively outputs crucial features via a gating unit. By replacing FPN with MSE-FPN in Faster R-CNN, our models achieve 39.4 and 41.2 Average precision (AP) using ResNet50 and ResNet101 as the backbone network respectively. When using ResNet-101-64x4d as the backbone, MSE-FPN achieved up to 43.4 AP. Our results demonstrate that replacing FPN with MSE-FPN significantly enhances the detection performance of state-of-the-art FPN-based detectors.
Binary image steganography based on permutation

Gray and color images contain much space for hiding information, and modification of pixel values within a certain range does not cause the image to appear abnormal. However, pixels in binary images possess only two states: black and white. As a result, distortions of binary images can be easily detected. For this reason, hiding information in binary images is more challenging. In this paper, we propose a steganographic scheme based on permutations, which improves the capacity of embedding information in a series of p host binary images. Consider a steganographic scheme T that can hide $$p\times Q(m, n)$$ bits of data in p binary images, where Q(m, n) represents the embedding capacity of T for an $$m\times n$$ binary image block. Our scheme takes advantage of permutation to improve this capacity for p binary images so that, instead of $$p\times Q(m, n)$$ bits, it can hide $$p \times \left\lfloor \log _2(p) \right\rfloor + p\times Q(m, n)$$ bits. The results obtained by experiments show that our model performs a better hiding process in terms of hiding capacity.
Laser Image Enhancement Algorithm Based on Improved EnlightenGAN

In distance-selected imaging, the contrast of laser images is reduced due to long imaging distances, insufficient laser power, and atmospheric turbulence. An enhancement algorithm based on the EnlightenGAN network is proposed to improve the contrast of laser images. Firstly, the laser images are acquired using a distance selection pass system to establish the laser image dataset and expand the dataset, and the traditional algorithm is used to enhance the images and establish the mapping relationship between low-quality images and high-quality images. The global discriminator based on PatchGAN with the improved VGG model is used to regularize the self-feature retention loss and construct the depth link between the global discriminator and the local discriminator to improve the generalization ability of the model; adjust the attention map to the second layer before the CLB convolution module and also add the residual structure in the second layer CLB to improve the robustness of the model; adopt the idea of gray-scale layering with a low drop and high rise to improve the self regularization mechanism to achieve the enhancement of the key region; finally, use the improved EnlightenGAN to fit the relationship between a low-quality image and high-quality image. Finally, EnlightenGAN is used to fit the relationship between low-quality images and high-quality images, extract laser image features, and enhance low-quality images. The experimental results show that the improved algorithm improves PSNR by 12.3% and 0.7% on average, SSIM by 57% and 10.3% on average, and NIQE by 21% and 13% on average compared to other algorithms and the original EnlightenGAN algorithm, respectively. The algorithm improves the signal-to-noise ratio and contrast of laser images with richer image details. It provides a new idea for pre-processing laser images.
Demystifying GPT and GPT-3: How they can support innovators to develop new digital accessibility solutions and assistive technologies?

GPT (Generative Pre-trained Transformer) is a neural network-based language model developed by OpenAI that has demonstrated impressive capabilities in generating human-like text and performing a wide range of natural language processing (NLP) tasks. GPT-3, the latest version of the model, is currently the largest and most advanced language model available, with 175 billion parameters. GPT and GPT-3 have the potential to support the development of digital accessibility solutions and assistive technologies, including text-to-speech synthesis, language translation, text summarization, and intelligent virtual assistants. In addition to its capabilities as a language model, GPT-3 has also been used as a tool for generating synthetic data and training other machine learning models. Some possible future directions for GPT include increased scale and performance, greater flexibility and adaptability, improved capabilities for unsupervised learning, and integration into more applications and industries.
Matrix Inverse-based Highly Payload Novel Approach for Covert Transmission

Background: The necessity for data security is demanding in the digital age. The main technique for achieving data security is Steganography. The technique of camouflaging the secret object behind another cover object is known as Steganography. A novel, robust, high payload, and imperceptible image steganography approach using matrix inverse in the spatial domain are proposed in this manuscript. The basic idea is to devise a robust novel approach against various image processing attacks, like cropping, compression, filters, and noise. Objectives: The study's objective is to develop a novel data-hiding approach that increases imperceptibility and payload capacity and is robust against various image processing attacks, like filters, compression, cropping, and noise. Methods: The matrix inverse procedure is used for the insertion and extraction of data. The symmetry feature of the matrix inverse makes the task of insertion of data simple and efficient. It also increases the hiding capacity while maintaining a finer level of imperceptibility and robustness. MATLAB is used for the implementation of the new technique and results analysis. Results: The proposed method's robustness has been analyzed against image processing assaults such as the inclusion of various noises, cropping, a variety of filters, and compression assaults. The imperceptibility of the approach has been tested successfully using PSNR, BER, and NCC metrics. The proposed method has been compared with the other two techniques. The experimental and comparison results depict that the proposed approach provides high hiding capacity, finer robustness, and imperceptibility. Conclusion: A novel, robust and imperceptible approach has been developed in this manuscript. The proposed method has been compared with the methods developed by Jung &amp; Yoo and Joshi &amp; Gill. The experimental findings show that the proposed technique offers better resilience, payload capacity, and finer imperceptibility.
An encoder‐decoder framework with dynamic convolution for weakly supervised instance segmentation

Abstract In the systems of industrial robotics and autonomous vehicles, instance segmentation is widely employed. However, manually labelling an object outline is time‐consuming. In order to reduce annotation costs, we present a weakly supervised instance segmentation method in this article. A deeply convolutional network is first used to construct multi‐scale feature maps for each object in the input image. After that, the encoder‐decoder framework with dynamic convolution is utilised to enhance model capacity and efficiency, while avoiding the issues of anchor design, proposal selection, and RoIAlign implementation. In particular, Dynamic Heads are used in the encoder to create dynamic convolution kernels, while Instance Heads are used in the decoder to provide the global feature map. With dynamic convolution, each instance can be segmented independently, reducing interference with other instances and improving segmentation accuracy. Under the supervision of projection loss and pixel point colour pairing loss, the contours of each object are finally outlined. On the PASCAL VOC and MS COCO datasets, the proposed method is competitive with more sophisticated approaches. In the VOC dataset, segmentation performance achieved 37.6% average precision with ResNet‐101 and FPN networks. The extensively visualised results demonstrate the effectiveness of the proposed encoder‐decoder framework with dynamic convolution.
A graph‐based method for Chinese address matching

Abstract Address matching is a substantial task in location‐based services. Currently, major address matching methods either perform rather badly on unstructured data or fail to extract adequate semantic information of address elements. In this article, we propose a graph‐based method that can deal with both sides of the problem. First, we use a pretrained transformer neural network to handle address tokenization. Then we parse address tokens into address elements according to their parts of speech. Then the node2vec and tf‐idf technique is used to generate node embeddings for each address element. Finally, an address matching graph convolutional network is applied to do the address matching work. We have carried out a series of experiments on a real‐world Chinese address corpus, to further evaluate the impacts of our methods. The experimental results indicate our method achieves higher scores than the state‐of‐the‐art methods.
‘Should’ve known better’: Using Lerner’s Belief in a Just World to understand how the Fraud Justice Network observe victims of online romance and investment frauds

This article explores the application of Lerner’s Belief in a Just World (BJW) to online fraud. BJW finds that people tend to blame victims when their sense of justice is threatened and if there is little or no chance of obtaining compensation or justice for the victim. Specifically, BJW suggests that when victims are perceived as acting with agency, and have little chance of compensation, they are blamed for their crime. Online fraud victims are blamed by family, friends, justice agencies, and themselves, for their victimisation. Little is known about why the Fraud Justice Network (FJN), which comprises the multitude of agencies that aim to combat online fraud, blames victims. This article begins to address this gap by presenting findings of qualitative interviews with 14 FJN representatives, using thematic analysis. Three significant themes emerge about victims: ‘agency’, ‘gullibility and compensation’, and ‘deservedness of justice’. This study found that (a) victims are seen as behaviourally responsible, (b) agency is blameworthy, and (c) victims deserve justice. The study also finds evidence of BJW thinking within the FJN. Findings can inform future organisational policies on how victims of online fraud can be more effectively managed and supported, including by financial institutions or law enforcement agencies.
EXPERIMENTAL STUDY OF THE INFLUENCE OF REGISTER FORMALITY ON THE PREFERRED REALIZATION OF THE COMPLEMENT CLAUSE IN RUSSIAN

In Russian, sentential complements (thatclauses) are optionally preceded by the demonstrative to ‘that’ (sometimes called ‘correlative’), with the required case marking or preposition selected by the verb. Several factors have been sug-gested in the literature as in uencing the choice of the construction with to, including the matrix predicate as well as information structure. is work investigates the in uence of register formality on the preferred realization of the complement. It is hypothesized that the construction with to, being the more explicit variant, is associated with more formal registers. is hypothesis has been previously tested on the basis of corpus data and received limited support. Here it is tested in a (two-al-ternative) forced-choice experiment where the register (news vs. spoken) is manipulated within items and subjects. 18 alternating predicates were tested, with two matched pairs of sentences with the contrasted registers constructed for each predicate. In addition, the experiment tested the in uence of lexical biases, as measured by the ‘collostructional analysis’ or by relative frequency of the alternating constructions in the corpus. e results showed a reliable e ect of lexical biases, as well as a much weaker but signi cant e ect of formality suggesting that in the news register the construction with to is preferred more o en compared to the spoken register, in line with the experimental hypothesis, whereas register did not interact with the lexical bias. Interestingly, a substantial amount of variation in the results was due to lexical biases as well as individual speaker preferences for one or the other construction.
Reconstructing variation in Indo-European word order

Abstract Word order is a central issue in the reconstruction of Proto-Indo-European syntax. Categorical approaches have proved to be inadequate because they postulate for the protolanguage a typological consistency which is absent in any of the attested daughter languages. Following recent research, we adopt a gradient approach to word order, which treats word order preferences as a continuous variable. We analyze four word order patterns based on data extracted from treebanks of ancient Indo-European languages. After presenting our results for AdpN/NAdp, GN / NG , AN / NA , and OV / VO , we draw a number of conclusions concerning variation within individual languages, crosslinguistic variation, and variation in diachrony that support the claim that variability should be taken as the normal state across languages, including reconstructed stages. We conclude that a non-discrete approach has the advantage of leading to a reconstruction that better conforms to the situation known from real languages, with variation as a key feature.
Oscillatory Kalman filtering for Duffing, Coulomb, and Van der Pol oscillators

The popularly known Gaussian filtering witnesses intractable integrals numerically approximated during the filtering. However, the numerical approximation methods used in the existing Gaussian filters are generally inaccurate for oscillatory and chaotic (OC) systems, resulting in poor accuracy. In this paper, we propose a new Gaussian filter named oscillatory spherical-radial Kalman filter (OSRKF) to improve the accuracy of OC systems. The proposed OSRKF decomposes the intractable integral into spherical and radial integrals. The spherical integral is approximated using the higher-degree spherical cubature rule, while the radial integral is approximated using exponentially-fitted Gauss–Laguerre quadrature rule. We also formulate the state estimation problems for three OC dynamical systems: the Duffing, Coulomb, and Van-der Pol oscillators. Subsequently, we validate the improved accuracy of the proposed OSRKF for all three OC dynamical systems.
Evaluation of the Brazilian Portuguese version of linguistic inquiry and word count 2015 (BP-LIWC2015)

Text psycholinguistic features are a valuable source for various research topics since they are used to obtain psychological, social, and linguistic aspects from written texts using dictionary files. These files are structured in categories, which are defined as groups of dictionary words that tap a particular domain (e.g., negative emotion words). The Linguistic Inquiry Word Count (LIWC) is a vastly used and versatile computer-based language analysis tool designed for text psycholinguistic analysis. The most recent version of the default English dictionary is LIWC2015, as it was released with the 2015 version of the LIWC software. The literature has recently introduced the latest Brazilian Portuguese LIWC dictionary (BP-LIWC2015), developed with the same categories as the LIWC 2015 English dictionary. However, the literature has also reported the need to evaluate BP-LIWC2015. In this scenario, this work investigates three questions: (i) Since LIWC2015 shows consistent improvements over the English dictionary developed in 2007 (LIWC2007), does BP-LIWC2015 achieves better text classification results than the older Brazilian Portuguese dictionary (BP-LIWC2007)? (ii) What is the equivalence between BP-LIWC2015 and BP-LIWC2007 with LIWC2015? (iii) Are there significant differences between Brazilian Portuguese dictionaries? To answer these questions, we conducted text classification experiments with four datasets and seven classification algorithms to compare the two Brazilian Portuguese LIWC dictionaries reported in the literature (i.e., 2007 and 2015). Second, we used a bilingual Portuguese-English scientific news collection to analyze the correlation between LIWC2015 and Brazilian Portuguese LIWC dictionaries. The results indicate that BP-LIWC2015 outperforms the older version in Brazilian Portuguese text classification. Finally, we found a more significant correlation between BP-LIWC2015 and the original English dictionary than the older version.
Multiscale Super-Resolution Remote Imaging via Deep Conditional Normalizing Flows

Many onboard vision tasks for spacecraft navigation require high-quality remote-sensing images with clearly decipherable features. However, design constraints and the operational and environmental conditions limit their quality. Enhancing images through postprocessing is a cost-efficient solution. Current deep learning methods that enhance low-resolution images through super-resolution do not quantify network uncertainty of predictions and are trained at a single scale, which hinders practical integration in image-acquisition pipelines. This work proposes performing multiscale super-resolution using a deep normalizing flow network for uncertainty-quantified and Monte Carlo estimates so that image enhancement for spacecraft vision tasks may be more robust and predictable. The proposed network architecture outperforms state-of-the-art super-resolution models on in-orbit lunar imagery data. Simulations demonstrate its viability on task-based evaluations for landmark identification.
A study on methods for revising dependency treebanks: in search of gold

Reliably annotated corpora with reliable annotation are a valuable resource for Natural Language Processing, which justifies the search for methods capable of assisting linguistic revision. In this context, we present a study on methods for revising dependency treebanks, investigating the contribution of three different strategies to the corpus review: (i) linguistic rules; (ii) an adaptation of the n-grams method proposed by Boyd et al. (2008) applied to Portuguese; and (iii) Inter-Annotator Disagreement, a linguistically motivated approach that draws inspiration from the human annotation process. The results are promising, and taken together the three methods can lead to the revision of up to 58% of the errors in a specific corpus at the cost of revising only 20% of the corpus. We also present a tool that integrates treebank editing, evaluation and search capabilities with the review methods, as well as a gold-standard Portuguese corpus from the oil and gas domain.