Reviewerly/exHarmony huggingface dataset:
Enhanced Convolutional Neural Network for solar radiation nowcasting: All-Sky camera infrared images embedded with exogeneous parameters

Electrical power production by renewable energy sources is unpredictable in nature and this may cause imbalance between power generation and demand. Therefore, an accurate prediction of solar radiation is crucial for the stability and efficient management of electric grid. This study focuses on very short-term forecasts of solar radiation with a horizon in the range of 5-15 min. In this paper, a Convolutional Neural Network is proposed that uses sequences of infrared images captured by an All-Sky Imager to forecast the Global Horizontal Irradiance on different time horizon. A real case study, exploiting six months of high-resolution data, is analysed. Additionally, an innovative technique, the Enhanced Convolutional Neural Network (ECNN), is proposed in which exogenous data, as the solar radiation measurement, is encoded in terms of coloured pixels in the upper corner of the images. Considering the naïve persistence method as a baseline, a clear improvement across the key metrics has been noted with the proposed methodology. A deeper analysis of the results reveals that the proposed models are more accurate than persistence when high fluctuations of solar radiation are experienced. In that case, the ECNN achieves a forecast skill exceeding 19% for all the tested forecast horizons.
Corner-to-Center long-range context model for efficient learned image compression

In the framework of learned image compression, the context model plays a pivotal role in capturing the dependencies among latent representations. To reduce the decoding time resulting from the serial autoregressive context model, the parallel context model has been proposed as an alternative that necessitates only two passes during the decoding phase, thus facilitating efficient image compression in real-world scenarios. However, performance degradation occurs due to its incomplete casual context. To tackle this issue, we conduct an in-depth analysis of the performance degradation observed in existing parallel context models, focusing on two aspects: the Quantity and Quality of information utilized for context prediction and decoding. Based on such analysis, we propose the Corner-to-Center transformer-based Context Model (C3M) designed to enhance context and latent predictions and improve rate–distortion performance. Specifically, we leverage the logarithmic-based prediction order to predict more context features from corner to center progressively. In addition, to enlarge the receptive field in the analysis and synthesis transformation, we use the Long-range Crossing Attention Module (LCAM) in the encoder/decoder to capture the long-range semantic information by assigning the different window shapes in different channels. Extensive experimental evaluations show that the proposed method is effective and outperforms the state-of-the-art parallel methods. Finally, according to the subjective analysis, we suggest that improving the detailed representation in transformer-based image compression is a promising direction to be explored.
Particle filtering for dynamic systems with future constraints

This work focuses on a new state estimation problem for dynamic systems with future constraints. It is well known that constraints, as a priori, should always be satisfied by states of some dynamic systems, and thus sufficiently fusing the valuable information into filters will produce more accurate estimates. To this end, a particle smoothing framework is proposed by incorporating future constraints, instead of only current constraints. Next, we construct a criterion function by making the center value of dynamic model satisfy constraints to improve dynamic modeling accuracy, and investigate a cost function to compensate for the impact of the modified dynamic model on the original predicted distribution. Based on this, we present a new particle filter that future constraints are applied in importance sampling and updating particle weights. Especially, the modified dynamic model is chosen as the importance density function so that the sampled particles satisfy constraints at consecutive times as much as possible. Finally, two simulations verify the superiority of the proposed filter.
Scalable and Accelerated Self-healing Control Circuit Using Evolvable Hardware

Controllers are mission-critical components of any electronic design. By sending control signals, they decide which and when other data path elements must operate. Faults, especially Single Event Upset (SEU) occurrence in these components, can lead to functional/mission failure of the system when deployed in harsh environments. Hence, competence to self-heal from SEU is highly required in the control path of the digital system. Reconfiguration is critical for recovering from a faulty state to a non-faulty state. Compared to native reconfiguration, the Virtual Reconfigurable Circuit (VRC) is an FPGA-generic reconfiguration mechanism. The non-partial reconfiguration in VRC and extensive architecture are considered hindrances in extending the VRC-based Evolvable Hardware (EHW) to real-time fault mitigation. To confront this challenge, we have proposed an intrinsic constrained evolution to improve the scalability and accelerate the evolution process for VRC-based fault mitigation in mission-critical applications. Experimentation is conducted on complex ACM/SIGDA benchmark circuits and real-time circuits used in space missions, which are not included in related works. In addition, a comparative study is made between existing and proposed methodologies for brushless DC motor control circuits. The hardware utilization in the multiplexer has been significantly reduced, resulting in up to a 77% reduction in the existing VRC architecture. The proposed methodology employs a fault localization approach to narrow the search space effectively. This approach has yielded an 87% improvement on average in convergence speed, as measured by the evolution time, compared to the existing work.
Generalised synchronisations, embeddings, and approximations for continuous time reservoir computers

We establish conditions under which a continuous time reservoir computer, such as a leaky integrator echo state network, admits a generalised synchronisation f between the source dynamics and reservoir dynamics. We show that multiple generalised synchronisations can exist simultaneously, and connect this to the multi-Echo-State-Property (multi-ESP). In the special case of a linear reservoir computer, we derive a closed form expression for the generalised synchronisation f. Furthermore, we establish conditions under which f is of class C1, and conditions under which f is a topological embedding on the fixed points of the source system. This embedding result is closely related to Takens' embedding Theorem. We also prove that the embedding of fixed points occurs almost surely for randomly generated linear reservoir systems. With an embedding achieved, we discuss how the universal approximation theorem makes it possible to forecast the future dynamics of the source system and replicate its topological properties. We illustrate the theory by embedding a fixed point of the Lorenz-63 system into the reservoir space using numerical methods. Finally, we show that if the observations are perturbed by white noise, the GS is preserved up to a perturbation by an Ornstein–Uhlenbeck process.
An anti-steganalysis adaptive steganography for HEVC video based on PU partition modes

Calibration-based steganalysis methods are a fatal threat to current HEVC steganography algorithms. The current steganography algorithms based on prediction unit (PU) still lack resistance to this type of steganalysis. The reason is that the improvements in HEVC codec provide more potential steganographic space as well as introduce more detectable distortion. Therefore, a HEVC video steganography method that has resistance to PU shift-based steganalysis is proposed in this paper. First, the PU shift phenomenon that exists in PU-based steganography is introduced. Then the existing calibration-based steganalysis method is enhanced. According to the encoding rules of HEVC, the distortion cost function according to the difference of rate distortion cost (RD cost) during recompression is designed, which aims to obtain better resistance to calibration-based steganalysis. Experimental results demonstrate that the steganography algorithm designed in this paper provides excellent resistance ability for calibration-based steganalysis.
Traffic spills the beans: A robust video identification attack against YouTube

Traffic-based video identification attack is a side-channel attack that allows an attacker to identify transmitted online videos from encrypted network traffic. Existing attacks face two types of challenges: 1) YouTube's improved streaming technology renders them invalid or impacted; 2) real-world diverse traffic makes them underperform. In this paper, we provide the first study of YouTube's technical improvements and reveal its three key features: i) the mechanism that replaces the MPD file, ii) variable combined segments, and iii) various buffering processes. Based on the above analysis, we propose a Robust and easily scalable Video Identification Attack against YouTube called RoVIA. RoVIA extracts video fingerprints from video file headers and reference sequences from video traffic. RoVIA uses the reference sequence to generate candidate sequences in the pre-built video fingerprint library and achieves video identification by measuring sequence similarity. Experimental results show that RoVIA achieves open-world accuracy of 0.967, 0.924, and 0.863 in open-world, fixed-quality, and poor network experiments. Furthermore, RoVIA's scalability allows the target classes to be adjusted by updating the fingerprint library without collecting new samples and retraining parameters. This study aims to demonstrate the vulnerabilities of YouTube streaming technology and the potential consequences of its malicious use. We also propose two YouTube-friendly countermeasures to help developers build effective defenses.
CTFCD: Channel transformer based on full convolutional decoder for single image deraining

Although convolutional neural network and visual transformer have been successfully applied in various field of computer vision, there is little work combining them to construct an efficient network model to solve image deraining tasks. Convolutional neural network is utilized to extract the features from each region, while visual transformer is utilized to extract the context information between local features. Due to the limitations in computational resources and processing time, it is difficult for visual transformer to process high-resolution images, which hinders the application of visual transformer in devices with limited hardware resources. The purpose of this article is to utilize the advantages of to design a lightweight encoder-decoder network for real-time image deraining. Firstly, a novel channel Transformer module is designed to obtain global contextual information, where deep separable convolution is utilized to extract multi-scale local features and a Transformer encoder is constructed by stacking Transformer modules. Secondly, a decoder based on a fully convolution is designed to adopt mask attention and inverted bottleneck convolution to achieve progressive feature fusion and feature reconstruction, which significantly reduces computational complexity and memory requirement. A large number of experimental results have verified that the proposed method has superior performance compared with other state-of-the art methods, while the computational cost and parameter quantity are much smaller than those of similar methods.
Modeling of PID controlled 3DOF robotic manipulator using Lyapunov function for enhancing trajectory tracking and robustness exploiting Golden Jackal algorithm

In this study, a three degrees of freedom (3 DOF) rigid-link robotic manipulator (RLM) has been simulated by using the Simscape model and the mathematical model derived by Lagrange method. The robot arm has been regulated by an Optimized PID Controller to achieve better tracking performance and reasonable robustness against disturbances and payload uncertainty. To optimize the controller, a novel nature-inspired Golden Jackal Optimization (GJO) algorithm has been used due to its efficient exploration that increases the diversity of the released solutions and its exploitation schemes which enhance the best-explored solutions. The tuning process has utilized a Lyapunov stability function as the objective function (OF) and the efficacy of the proposed algorithm is evaluated through a comprehensive comparison with various state-of-the-art metaheuristic techniques such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Jellyfish Search Optimizer (JSO), Whale Optimization Algorithm (WOA), Arithmetic Optimization Algorithm (AOA) and Sine Cosine Algorithm (SCA). The assessment has been conducted on benchmark error-based functions, providing rigorous testing and validation of the algorithm's performance. Furthermore, the performance evaluation has focused on the system's robustness against disturbances, noise, and variations in the payload mass, particularly in the context of Pick and Place (PNP) industrial tasks. The results of simulation have demonstrated that the optimized system, employing the Lyapunov function, demonstrated superior performance in minimizing the objective function value compared to other benchmark functions.
YembaTones: A syllable-tone annotated dataset for speech recognition and prosodic analysis of the Yemba language

Prosody is a key area of linguistics that explores tonal and rhythmic variations in speech. In tonal languages such as Yemba, prosody plays a crucial role in distinguishing between words with different meanings or different grammatical forms. However, despite the large number of native speakers of this language in Cameroon, there are few resources for the speech recognition and synthesis. In this article, we present YembaTones, a syllabic and tonal annotated dataset, created from a dictionary we designed of 344 Yemba/French words coming from the most common phrases of the language, grouped according to their spellings that only differ by the tone. The dataset was originally designed for training and evaluating tone detection models for tonal and low resource languages. The recordings of the pronunciation of these words were made with 11 native speakers of Yemba, mainly specialists in linguistics with a good command of the sounds of the language. The recordings were made with a dictaphone in different places such as the homes of the speakers, the campuses and their workplaces. Then they have been cleaned and segmented into individual audio files corresponding to the pronunciations of isolated words, using the software Audacity. After cleaning and segmentation, we selected 3420 good quality audio files for annotation. Annotations were made at the syllabic and tonal level using Praat software. YembaTones is a valuable resource not only for the training and evaluation of automatic tone detection models but also for automatic speech recognition, speech synthesis of tonal and poorly endowed languages, as well as for the study of prosody and Yemba phonetics, research in speech acoustics and phonetic linguistics.
Algorithms for 2-club cluster deletion problems using automated generation of branching rules

In the 2-Club Cluster Vertex Deletion (resp., 2-Club Cluster Edge Deletion) problem the input is a graph G and an integer k, and the goal is to decide whether there is a set of at most k vertices (resp., edges) whose removal from G results in a graph in which the diameter of every connected component is at most 2. In this paper we give algorithms for 2-Club Cluster Vertex Deletion and 2-Club Cluster Edge Deletion whose running times are O⁎(3.104k) and O⁎(2.562k), respectively. Our algorithms were obtained using automated generation of branching rules. Our results improve the previous O⁎(3.303k)-time algorithm for 2-Club Cluster Vertex Deletion [Liu et al., FAW-AAIM 2012] and the O⁎(2.695k)-time algorithm for 2-Club Cluster Edge Deletion [Abu-Khzam et al., TCS 2023].
Efficient approach of high average utility pattern mining with indexed list-based structure in dynamic environments

Various studies on high utility pattern mining have been conducted to satisfy the emerging need to consider the characteristics of real-world databases, such as the importance and quantity of items. In the traditional utility-based framework, the mining result is influenced by the number of items in a pattern, or in some cases, single utilities of items. In order to overcome this drawback, high average utility pattern mining has been proposed. It provides more interesting results since it takes into account the average utility of patterns by considering their lengths. Methods based on this concept have emerged in recent years, including ones that target incremental environments. However, existing algorithms create an enormous number of candidate patterns or require complex operations during the mining process. To address this degradation, we propose a new and more efficient approach for mining high average utility patterns in dynamic environments. The proposed algorithm utilizes a data structure more efficient than previous ones, which takes the form of an indexed list. It also incorporates efficient realigning and mining techniques for handling incremental data and accurately mining results. Experimental results show the superiority of the proposed approach in terms of runtime, memory usage, scalability, and accuracy.
Geochemical prospectivity mapping using compositional balance analysis and multifractal modeling: A case study in the Jinshuikou area, Qinghai, China

To effectively map the prospecting targets related to deposit-type sought, local indicators of spatial association (LISA), compositional balance analysis (CoBA), and multifractal modeling were applied to process stream sediment geochemical data that are collected from the Jinshuikou area, Qinghai province, China. In this study, 14 elements, including Zn, Cu, Ni, Mo, Co, V, Ti, Sb, Ag, Pb, Sn, Hg, Cd, and W, were analyzed in 493 samples. The result of LISA shows that the ore-forming elements Zn and Cu have a close spatial autocorrelation, which supports the construction of balance b1 related to the Zn-Cu mineralization assemblage. A traditional contrast additive method was applied to compare with balance b1 for geochemical anomaly identification. The results clearly indicate that balance b1 significantly assisted with not only reduction of geochemical data internal imbalance caused by extreme value, but also intensification of Zn-Cu assemblage geochemical anomalies associated with Zn polymetallic mineralization. The high anomalies of the two balances, b5 (Co-V-Ti) and b8 (Sn-Pb-Ag-Sb), reflect well the distribution of basic and intermediate-acid rocks in the area, respectively. The geochemical distribution pattern of the balance b1 can be depicted by multifractal parameters such as singular index α, as well as asymmetry index R, which are useful for describing the spatial heterogeneity of the balance b1. The singularity indices of balance b1 were classified by the concentration-area (C-A) fractal model to produce the high potential areas of the Zn-Cu assemblage. The high potential areas were obviously correlated with all Zn polymetallic occurrences. So, combined with the metallogenic geological characteristics of the study area, five prospective prediction targets were mapped. The anomaly verification demonstrates that the Zn concentration has reached the lowest industrial grade and cut-off grade corresponding to the target area I-1 and I-2, respectively, and Cu also has certain metallogenic potential. There is good potential for Zn-Cu mineralization as represented by this integrated method.
Spatial distribution of molybdenum in catchment sediments in the Sino-Mongolian border region

The Sino-Mongolian border region is an important exploration hotspot in the Asia-Pacific margin and hosts one of the three major porphyry Cu-Au-Mo mineralization belts in the world. Variations in the concentration and spatial distribution of Mo in the region can indirectly reflect the distribution of other minerals, such as copper; therefore, Mo has been widely used as a pathfinder element for geochemical prospecting for porphyry Cu deposits. However, comprehensive and integrated analytical studies of single elements (e.g., Mo) in the Sino-Mongolian border region are lacking. Therefore, this study collected 10,453 catchment sediment samples from the study region and analyzed 69 elements. The concentration of Mo in these samples ranged from 0.10 to 84.96 mg/kg, with a median and mean of 0.84 mg/kg (considered the geochemical background value) and 0.97 mg/kg, respectively. The factors influencing the Mo concentration and spatial distribution along the Sino-Mongolian border region were investigated in terms of geological background, climate, precipitation, mineral distribution, and exploitation history. Influenced by climate and precipitation, Mo in catchment sediments is solubilized and easily transported under the oxidizing effect of hypergenesis. Mo concentrations were low in forested areas along the Altai Mountains in the west and Da Hinggan Ling Mountains in the east. Sixteen high-concentration Mo areas (geochemical province/domain, > 1.07 mg/kg, > Q75%) that contained 46 Mo geochemical anomalies (> 1.24 mg/kg, > Q85%) were circumscribed in this study. These anomalous areas were mainly related to the widespread distribution of minerals, coal-forming strata, and black shale deposits in the region. Compared to previous studies, this study identified more concentrated geochemical anomaly areas with a wider distribution in the Sino-Mongolian border region and provided higher quality geochemical data to promote further mineral exploration and support environmental change studies in this area.
A filter-augmented auto-encoder with learnable normalization for robust multivariate time series anomaly detection

While existing reconstruction-based multivariate time series (MTS) anomaly detection methods demonstrate advanced performance on many challenging real-world datasets, they generally assume the data only consists of normal samples when training models. However, real-world MTS data may contain significant noise and even be contaminated by anomalies. As a result, most existing approaches easily capture the pattern of the contaminated data, making identifying anomalies more difficult. Although a few studies have aimed to mitigate the interference of the noise and anomalies by introducing various regularizations, they still employ the objective of fully reconstructing the input data, impeding the model from learning an accurate profile of the MTS's normal pattern. Moreover, it is difficult for existing methods to apply the most appropriate normalization schemes for each dataset in various complex scenarios, particularly for mixed-feature MTS. This paper proposes a filter-augmented auto-encoder with learnable normalization (NormFAAE) for robust MTS anomaly detection. Firstly, NormFAAE designs a deep hybrid normalization module. It is trained with the backbone end-to-end in the current training task to perform the optimal normalization scheme. Meanwhile, it integrates two learnable normalization sub-modules to deal with the mixed-feature MTS effectively. Secondly, NormFAAE proposes a filter-augmented auto-encoder with a dual-phase task. It separates the noise and anomalies from the input data by a deep filter module, which facilitates the model to only reconstruct the normal data, achieving a more robust latent representation of MTS. Experimental results demonstrate that NormFAAE outperforms 17 typical baselines on five real-world industrial datasets from diverse fields.