Reviewerly/exHarmony huggingface dataset:
Intelligent Underwater Object Detection and Image Restoration for Autonomous Underwater Vehicles

Unmanned Underwater Vehicles (UUVs) have been reliable and economical technological solutions to perform undersea monitoring tasks in comparison to manned vehicles. However, in many situations, UUV is unable to fulfill complex undersea research tasks since target objects appear distorted due to light absorption and scattering. Besides, ocean surveying undergoes severe power requirements compared to terrestrial systems because of battery-driven low-storage vehicles like Unmanned Underwater Vehicles (UUVs). Therefore, limited power supply, motion resistance of water medium, and distorted target object appearance can delay the mission and reduce the efficiency of UUV in their underwater operations. Considering the resource-constrained undersea monitoring setup, we propose an intelligent two-stage framework for expeditious monitoring of underwater scenes. First, an effective deep neural network is employed for underwater object/region of interest (ROI) detection. Then the detected ROI is restored using an efficient restoration method, thereby improving the visual quality of the degraded images and aiding the navigating and monitoring tasks of UUVs. Our method has been objectively and subjectively assessed using 9 evaluation metrics and our key results reveal mAP of 94.35% and an Underwater Color Image Quality Evaluation (UCIQE) score of 3.09, surpassing state-of-the-art methods for object detection. Furthermore, the execution time of 0.550 secs is required for object detection and dehazing, making this proposal suitable for UUVs to perform automatic undersea object detection and dehazing within operational running requirements.
A Defective Bolt Detection Model With Attention-Based RoI Fusion and Cascaded Classification Network

In the Unmanned Aerial Vehicle (UAV) transmission line inspection images, the detection of defective small-size object such as bolts on towers is important and challenging. Although using multi-scale features of deep neural network has improved the performance, it is still inadequate in mining fine-grained associations between multi-scale features and dealing with the high similarity between normal and defective bolts. Therefore, this paper proposes an improved defective bolt detection model MARF-CCN, based on Region of Interest (RoI) feature fusion and Cascaded Classification Network (CCN). First, a mixed attention RoI fusion network is built to adaptively compute fine-grained weights for features at different scales of feature pyramid network and enhances the difference between foreground and background. Second, cascaded classification network is designed to divide the original classification results into more easily identifiable categories based on morphological features, which are rectified via a secondary classifier to reduce false detection. Third, this paper defines atypical defects based on occurrence frequency and utilizes Focal Loss to address the resulting imbalanced classification loss. Experiments show that MARF-CCN improves the Average Precision (AP) of defective bolts by 14.33% to 84.40% compared with the commonly used models.
DAON: A Decentralized Autonomous Oracle Network to Provide Secure Data for Smart Contracts

Blockchain, which originated with the Bitcoin system, has drawn intense attention because of its decentralization, persistence, and anonymity. The execution environment of blockchain is isolated from the external world and requires "blockchain oracles": agents that fetch information from the out-side world. However, there is always the risk of oracles providing corrupt, malicious, or inaccurate data. To overcome this issue, this paper analyses the existing oracle working patterns, then introduces a decentralized autonomous oracle network (DAON) and its consensus protocol and noninteractive reputation maintenance and payment scheme. Meanwhile, the reputation and security monitoring services of DAON are designed to ensure the DAON provide trustworthy data services in a complex byzantine environment. The proposed method is verified by experiments on three applications. Using the proposed network, smart contracts on future blockchains could have reliable, tamper-proof inputs and outputs.
Hyperspectral Denoising via Global Variation and Local Structure Low-Rank Model

Hyperspectral images (HSIs) are often disturbed by various kinds of noises. This paper proposes a global variation and local structure low-rank model (GLLR) for HSI denoising by integrating spatial segmentation smoothing and spectral low-rank properties. Compared with existing denoising methods, the proposed method considers not only the global low-rank property but also the local structure low-rank property of HSIs. Specifically, the GLLR describes the global correlation and segmental smoothing structure of the HSI by the correlated total variation. In addition, we construct a new structural low-rank prior, called the local minimum difference (LMD) low-rank. With LMD low-rank property of HSI, GLLR can remove noise while retaining useful structural information in the HSI. Then, an ALM-based optimization algorithm is devised to solve the objective functions for the presented model. Finally, comparison experiments with existing methods are conducted on synthetic and real datasets to demonstrate the effectiveness and superiority of the proposed method.
MSFFA-YOLO Network: Multiclass Object Detection for Traffic Investigations in Foggy Weather

Despite significant progress in vision-based detection methods, the task of detecting traffic objects in foggy weather remains challenging. The presence of fog reduces visibility, which in turn affects the information of traffic objects in videos. However, accurate information regarding the localization and classification of traffic objects is crucial for certain traffic investigations. In this paper, we focus on presenting a multi-class object detection method, namely MSFFA-YOLO network, that can be trained and jointly achieve three tasks: visibility enhancement, object classification, and object localization. In the network, we employ the enhanced YOLOv7 as a detection subnet, which is responsible for learning to locate and classify objects. In the restoration subnet, the multi-scale feature fusion attention (MSFFA) structure is presented for visibility enhancement. The experimental results on the synthetic foggy datasets show that the presented MSFFA-YOLO can achieve 64.6 percent accuracy on the FC005 dataset, 67.3 percent accuracy on the FC01 dataset, and 65.7 percent accuracy on the FC02 dataset. When evaluated on the natural foggy datasets, the presented MSFFA-YOLO can achieve 84.7 percent accuracy on the RTTS dataset and 84.1 percent accuracy on the RW dataset, indicating its ability to accurately detect multi-class traffic objects in real and foggy weather. And the experimental results show that the presented MSFFA-YOLO can achieve the efficiency of 37 FPS. Lastly, the experimental results demonstrate the excellent performance of our presented method for object localization and classification in foggy weather. And when detecting concealed traffic objects in foggy weather, our presented method exhibits superior accuracy. These results substantiate the applicability of our presented method for traffic investigations in foggy weather.
Automatic Helmet (Object) Detection and Tracking the Riders Using Kalman Filter Technique

The rise in video content has led to the development of numerous applications, such as video summarization, the extraction of useful content from videos, the creation of autonomous video surveillance systems, etc. A typical use for such software is the analysis of object recognition and tracking in videos. Researchers have suggested a few clever ways in this context, including background detection, frame difference, and optical flow-based methods. Nevertheless, the problem persists because of a number of challenges that make identification and tracking challenging, including camera jitter, quick changes in lighting, dynamic backdrops, moving cameras, and shadow detection. Many problems cannot be resolved by ordinary algorithms because of the complexity, imprecision, and improbable elements introduced at numerous intermediary stages. In an effort to address these issues, studies based on soft computing have gained prominence. These studies have demonstrated considerable advancements in the solution of problems that are difficult to handle using traditional approaches. This chapter will present a thorough analysis of contemporary technologies for object detection and tracking with a focus on soft computing-based techniques. A Kalman filter-based tracker is implemented to predict and track various trajectories of a moving object. The proposed method is tested on benchmark datasets, and both qualitative and quantitative results are presented. For normal videos, there has been an overall increase in value 80% accuracy as compared to contemporary techniques. The results indicate the dominance of the proposed method in handling various complex challenges also.
Reproducing “Show, Attend and Tell: Neural Image Caption Generation with Visual Attention”

Abstract This paper replicates the experiment presented in the work of Xu et al. [1], and examines errors in the generated captions. The analysis of the identified errors aims to provide deeper insight into the underlying causes. This study also encompasses subsequent experiments aiming at investigating the feasibility of rectifying these errors via a post-processing stage. Image recognition and object detection models, as well as a language probability computational model were explored. The findings presented in this paper aim to contribute towards the overarching objective of Explainable Artificial Intelligence (XAI), thereby providing potential pathways to improve image captioning.
Correlation Matrix Clustering for Statistical Arbitrage Portfolios

We propose a framework to construct statistical arbitrage portfolios with graph clustering algorithms. First, we use various clustering methods to partition the correlation matrix of market residual returns of stocks into clusters. Next, we construct and evaluate the performance of mean-reverting statistical arbitrage portfolios within each cluster. We explore five clustering algorithms and demonstrate that our proposed framework generates profitable trading strategies with over 10% annualized returns and statistically significant Sharpe ratios above one. The performance of our statistical arbitrage portfolios is neutral to the market and cannot be fully explained by intra-industry mean-reversion effects.
Technocamps: 30 Years of Digital Education and Professional Development Throughout Wales

We describe Technocamps, an award-winning, long-running universities-based pan-Wales school and community outreach programme.
$$\textsf{Testudo}$$: Linear Time Prover SNARKs with Constant Size Proofs and Square Root Size Universal Setup

We present Testudo, a new FFT-less SNARK with a near linear-time prover, constant-time verifier, constant-size proofs and a square-root-size universal setup. Testudo is based on a variant of Spartan [28]–and hence does not require FFTs–as well as a new, fast multivariate polynomial commitment scheme (PCS) with a square-root-sized trusted setup that is derived from PST [25] and IPPs [9]. To achieve constant-size SNARK proofs in Testudo we then combine our PCS openings proofs recursively with a Groth16 SNARK. We also evaluate Testudo and its building blocks: to compute a PCS opening proof for a polynomial of size $$2^{25}$$ , our new scheme opening procedure achieves a 110x speed-up compared to PST and 3x compared to Gemini [6], since opening computations are heavily parallelizable and operate on smaller polynomials. Furthermore, a Testudo proof for a witness of size $$2^{30} ({\approx } 1\,\text {GB})$$ requires a setup of size only $$2^{15}$$ ( $$\approx $$ tens of kilobytes). Finally, we show that a Testudo variant for proving data-parallel computations is almost 10x faster at verifying $$2^{10}$$ Poseidon-based Merkle tree opening proofs than the regular version .
Composable Oblivious Pseudo-random Functions via Garbled Circuits

Oblivious Pseudo-Random Functions (OPRFs) are a central tool for building modern protocols for authentication and distributed computation. For example, OPRFs enable simple login protocols that do not reveal the password to the provider, which helps to mitigate known shortcomings of password-based authentication such as password reuse or mix-up. Reliable treatment of passwords becomes more and more important as we login to a multitude of services with different passwords in our daily life. To ensure the security and privacy of such services in the long term, modern protocols should always consider the possibility of attackers with quantum computers. Therefore, recent research has focused on constructing post-quantum-secure OPRFs. Unfortunately, existing constructions either lack efficiency, or they are based on complex and relatively new cryptographic assumptions, some of which have lately been disproved. In this paper, we revisit the security and the efficiency of the well-known "OPRFs via Garbled Circuits" approach. Such an OPRF is presumably post-quantum-secure and built from well-understood primitives, namely symmetric cryptography and oblivious transfer. We investigate security in the strong Universal Composability model, which guarantees security even when multiple instances are executed in parallel and in conjunction with arbitrary other protocols, which is a realistic scenario in today's internet. At the same time, it is faster than other current post-quantum-secure OPRFs. Our implementation and benchmarks demonstrate that our proposed OPRF is currently among the best choices if the privacy of the data has to be guaranteed for a long time.
Broadcast-Optimal Two Round MPC with Asynchronous Peer-to-Peer Channels

In this paper we continue the study of two-round broadcast-optimal MPC, where broadcast is used in one of the two rounds, but not in both. We consider the realistic scenario where the round that does not use broadcast is asynchronous. Since a first asynchronous round (even when followed by a round of broadcast) does not admit any secure computation, we introduce a new notion of asynchrony which we call $$(t_{d}, t_{m})$$ -asynchrony. In this new notion of asynchrony, an adversary can delay or drop up to $$t_{d}$$ of a given party's incoming messages; we refer to $$t_{d}$$ as the deafness threshold. Similarly, the adversary can delay or drop up to $$t_{m}$$ of a given party's outgoing messages; we refer to $$t_{m}$$ as the muteness threshold. We determine which notions of secure two-round computation are achievable when the first round is $$(t_{d}, t_{m})$$ -asynchronous, and the second round is over broadcast. Similarly, we determine which notions of secure two-round computation are achievable when the first round is over broadcast, and the second round is (fully) asynchronous. We consider the cases where a PKI is available, when only a CRS is available but private communication in the first round is possible, and the case when only a CRS is available and no private communication is possible before the parties have had a chance to exchange public keys.
Effective Pairings in Isogeny-Based Cryptography

Pairings are useful tools in isogeny-based cryptography and have been used in SIDH/SIKE and other protocols. As a general technique, pairings can be used to move problems about points on curves to elements in finite fields. However, until now, their applicability was limited to curves over fields with primes of a specific shape and pairings seemed too costly for the type of primes that are nowadays often used in isogeny-based cryptography. We remove this roadblock by optimizing pairings for highly-composite degrees such as those encountered in CSIDH and SQISign. This makes the general technique viable again: We apply our low-cost pairing to problems of general interest, such as supersingularity verification and finding full-torsion points, and show that we can outperform current methods, in some cases up to four times faster than the state-of-the-art. Furthermore, we analyze how pairings can be used to improve deterministic and dummy-free CSIDH. Finally, we provide a constant-time implementation (in Rust) that shows the practicality of these algorithms.
On the Algebraic Immunity of Weightwise Perfectly Balanced Functions

In this article we study the Algebraic Immunity (AI) of Weightwise Perfectly Balanced (WPB) functions. After showing a lower bound on the AI of two classes of WPB functions from the previous literature, we prove that the minimal AI of a WPB n-variables function is constant, equal to 2 for $$n\ge 4$$ . Then, we compute the distribution of the AI of WPB function in 4 variables, and estimate the one in 8 and 16 variables. For these values of n we observe that a large majority of WPB functions have optimal AI, and that we could not obtain a WPB function with AI 2 by sampling at random. Finally, we address the problem of constructing WPB functions with bounded algebraic immunity, exploiting a construction from [12]. In particular, we present a method to generate multiple WPB functions with minimal AI, and we prove that the WPB functions with high nonlinearity exhibited in [12] also have minimal AI. We conclude with a construction giving WPB functions with lower bounded AI, and give as example a family with all elements with AI at least $$n/2-\log (n)+1$$ .
Theory of additive complementary dual codes, constructions and computations

We study complementary dual Fq-linear codes defined over an extension field Fqm, which we refer to as additive complementary dual (ACD) codes. We first consider duality of this class of codes with respect to a general inner product on Fqmn, which covers the commonly studied cases such as the trace-Euclidean and Hermitian inner products. In general this inner product is not necessarily symmetric, hence we define the notions of left/right hulls and left/right duals of an additive code. This leads to one-sided ACD codes, for which we prove a characterization result in terms of the generator matrix, extending the analogous characterization of Massey for linear complementary dual (LCD) codes. Then we focus on constructions and parameters of ACD codes with respect to trace-Euclidean, Hermitian and Galois inner products. We prove how LCD codes yield ACD codes relative to inner products considered in this work, make some observations on MDS subclass of ACD codes and present a construction of an ACD code by expanding another ACD code. We provide extensive computational results and tables on ACD codes over F4,F8 and F9. Our computations yield many MDS ACD codes and also examples of ACD codes, which have more codewords than the comparable LCD codes with the same length and minimum distance.
CURA: Real Time Artificial Intelligence and IoT based Fall Detection Systems for patients suffering from Dementia

According to the rising concern of the effects on the families due to dementia suffering patients, we aim to provide caretakers a work-life balance in which monitoring can be done with much more ease and efficiency in real time. This device can also be used in old age homes as well as hospitals which reduces the workload of the caretakers and helps them to easily monitor the patients. We aim to contribute for the betterment of the society and provide a virtual assistance for the patients suffering from dementia. The number of elderly people living alone has been increasing all over the world. If dementia has been detected at an early stage, the progress of disease can be slowed. The patients suffering from dementia are prone to falling quite frequently so as to detect that and to alert their caretakers to take necessary actions. In this study, we proposed a system in which we detect the real time state of the elderly people living alone by using the Machine Learning and IoT (Internet of Things) technology.We installed sensors inside a finger strap which is attached to the person. These sensors can detect the motions of the patient and predict their real time state to have a 24 by 7 support to provide assistance to the patients.
Learning and approximating piecewise smooth functions by deep sigmoid neural networks

Constructing neural networks for function approximation is a classical and longstanding topic in approximation theory, so is it in learning theory. In this paper, we are going to construct a deep neural network with three hidden layers using sigmoid function to approximate and learn the piecewise smooth functions, respectively. In particular, we prove that the constructed deep sigmoid nets can reach the optimal approximation rate in approximating the piecewise smooth functions with controllable parameters but without saturation. Similar results can also be obtained in learning theory, that is, the constructed deep sigmoid nets can also realize the optimal learning rates in learning the piecewise smooth functions. The above two obtained results underlie the advantages of deep sigmoid nets and provide theoretical assessment for deep learning.
Digital versus Paper Reading: A Systematic Literature Review on Contemporary Gaps According to Gender, Socioeconomic Status, and Rurality

This paper presents a comprehensive review of the literature on electronic reading (e-reading) versus paper reading. The main objective was to assess the current state of research comparing digital and paper reading outcomes among students aged 6-18 years old, as well as assessing the impact of various factors (gender, socioeconomic status, and school location) in explaining the differences between the two modes. Inclusion criteria included the following: participants (6-18 years), research focus (comparing digital reading and paper reading), study type (quantitative or mixed methods), publication (peer reviewed between 2015 and 2022), and language (English). A systematic search in four databases (WOS, Scopus, ERIC, and JSTOR) in August 2022 was conducted by three reviewers. The search revealed 23 studies matching the inclusion criteria. The findings from the reviewed studies are diverse, with some reporting no significant differences in reading comprehension between the two modes, while others suggest screen inferiority, thereby favoring paper reading. Individual-level predictors, such as prior comprehension skills and reading habits, play a crucial role in determining reading performance across modes. Family-level factors, such as the number of books at home, and school-level factors, like the usage of ICT resources, influence both paper and digital reading comprehension. Moreover, gender differences in attitudes and performance towards different reading modes are apparent. SES is positively associated with reading achievement in both modes, with a larger effect shown for paper reading. Overall, the comparison between electronic and paper reading modes reveals a complex interplay of individual and contextual factors influencing reading comprehension and attitudes.