Reviewerly/exHarmony huggingface dataset:
Resource-Efficient Federated Learning With Non-IID Data: An Auction Theoretic Approach

Federated learning (FL) has gained significant importance for intelligent applications, following data produced on a massive scale by numerous distributed IoT devices. From an FL perspective, the key aspect is that this data is not identically and independently distributed (IID) across different data sources and locations. This distribution-skewness leads to significant quality degradation. Moreover, an intrinsic consequence of using such non-IID data in decentralized learning is increasing costs that would be mitigated if using IID data. As a remedy, we propose a resource-efficient method for training an FL-based application with non-IID data, effectively minimizing cost through an auction approach and mitigating quality degradation through data sharing. In an experimental evaluation, we investigate the FL performance using real-world non-IID data and use the resulting ground-truth outputs to develop functions for estimating the utility of non-IID data, computational resource costs, and data generation costs. These functions are used to optimize the costs of model training, ensuring resource efficiency. It is further demonstrated that using shared-IID data significantly increases the resource efficiency of FL with local non-IID data. This holds true even when the shared IID data size is less than 1% of the size of the local non-IID data. Moreover, this work demonstrates that the profitability of the stakeholders can be maximized using the proposed auction procedure. The integration of the auction procedure and a resource-efficient training strategy allows FL service providers to create practical trading strategies by minimizing the FL clients’ resources and payments in a machine learning marketplace.
Research on efficient feature extraction: Improving YOLOv5 backbone for facial expression detection in live streaming scenes

Facial expressions, whether simple or complex, convey pheromones that can affect others. Plentiful sensory input delivered by marketing anchors' facial expressions to audiences can stimulate consumers' identification and influence decision-making, especially in live streaming media marketing. This paper proposes an efficient feature extraction network based on the YOLOv5 model for detecting anchors' facial expressions. First, a two-step cascade classifier and recycler is established to filter invalid video frames to generate a facial expression dataset of anchors. Second, GhostNet and coordinate attention are fused in YOLOv5 to eliminate latency and improve accuracy. YOLOv5 modified with the proposed efficient feature extraction structure outperforms the original YOLOv5 on our self-built dataset in both speed and accuracy.
Remote sensing techniques and geochemical constraints on the formation of the Wadi El-Hima mineralized granites, Egypt: new insights into the genesis and accumulation of garnets

Abstract The Wadi El-Hima Neoproterozoic I- and A-type granites in the Southern Eastern Desert of Egypt are rich in garnets (up to 30 vol%) and are cut by NW–SE strike-slip faults, as confirmed from structure lineament extraction maps. These mineralized granites and garnet mineralization zones can be successfully discriminated using remote sensing techniques. Spectral angle mapper and matched filtering techniques are highly effective for mapping garnet-rich zones and show that the highest garnet concentrations occur along the intrusive contact zone of NW–SE striking faults. El-Hima granites have high SiO 2 (73.5–75.1 wt%), Al 2 O 3 (13.4–15.3 wt%) and total alkali (6.7–8.7 wt%) contents, suggesting that they were sourced from peraluminous (A/CNK &gt; 1) parental magmas. Garnet-bearing trondhjemites are metasomatic in origin and formed after I-type tonalite-granodiorites, which originated in a volcanic arc tectonic setting. Garnet-rich syenogranites and alkali-feldspar granites are both post-collisional A-type granites: the syenogranites formed from peraluminous magmas generated by partial melting of lower crustal tonalite and metasedimentary protoliths during lithospheric delamination, and the alkali-feldspar granites crystallized from highly fractionated, felsic and alkali-rich peraluminous magmas in the upper crust. Garnets in El-Hima mineralized granites occur in three forms: (1) subhedral disseminated crystals, (2) vein-type crystals, and (3) aggregated subhedral crystals, reflecting different mechanisms of accumulation. All are dominantly almandine in composition (Alm 76 Sps 10 Prp 7 Grs 6 Adr 1 ) and have high average concentrations of heavy rare earth elements (HREE) (ΣHREE = 1636 ppm), Y = (3394 ppm), Zn (325 ppm), Li (39.17 ppm) and Ga (34.94 ppm). Garnet REE patterns show strong negative Eu anomalies with HREE enriched relative to LREE, indicating a magmatic origin. These magmatic garnets are late-stage crystallization products of Al-rich hydrous magmas, and formed at low temperature (680–730 °C) and pressure (2.1–2.93 kbar) conditions in the upper continental crust. Peculiar garnet concentrations in syenogranites near and along contact zones with alkali feldspar granites are related to peraluminous parent hydrous magma compositions. These garnets formed by in situ crystallization from A-type granite melts, alongside accumulation of residual garnets left behind after partial melting of the host garnet-rich granites along the intrusive contact. Magmatic-fluid flow along the NW–SE striking fault of Najd system enhanced garnet accumulation in melts, which formed clots and veins of garnet.
Solar irradiance forecasting using a novel hybrid deep ensemble reinforcement learning algorithm

Solar irradiance forecasting is a major priority for the power transmission systems in order to generate and incorporate the performance of massive photovoltaic plants efficiently. As such, prior forecasting techniques that use classical modelling and single deep learning models that undertake feature extraction procedures manually were unable to meet the output demands in specific situations with dynamic variability. Therefore, in this study, we propose an efficient novel hybrid solar irradiance forecasting model based on three steps. In the first step, we employ a powerful variable input selection strategy named as partial mutual information (PMI) to calculate the linear and non-linear correlations of the original solar irradiance data. In the second step, unlike the traditional deep learning models designing their architectures manually, we utilize several deep long short term memory-convolutional neural network (LSTM-CNN) models optimized by a novel modified whale optimization algorithm in order to compute the forecasting results of the solar irradiance datasets. Finally, in the third step, we deploy a deep reinforcement learning strategy for selecting the best subset of the combined deep optimized LSTM-CNN models. Through analysing the forecasting results over two real-world datasets gathered from the USA solar irradiance stations, it can be inferred that our proposed algorithm outperforms other powerful benchmarked algorithms in 1-step, 2-step, 12-step, and 24-step ahead forecasting.
Incremental learning for transductive support vector machine

Semi-supervised learning is ubiquitous in real-world machine learning applications due to its good performance for handling the data where only a few number of samples are labeled while most of then are unlabeled. Transductive support vector machine (TSVM) is an important semi-supervised learning method which formulates the problem as a nonconvex combinatorial optimization problem. The infinitesimal annealing algorithm is a novel training method of TSVM which can alleviate the impact of the combinatorial and non-convex natures in TSVM and achieve a fast training of TSVM. However, it is still a challenging problem to handle large-scale data for TSVM even using the infinitesimal annealing algorithm. To mitigate this problem, in this paper, we propose an incremental learning algorithm for TSVM (ILTSVM) based on the path following technique under the framework of infinitesimal annealing. Specifically, for new samples, we call CP-Step to change the solution and partition by increasing the size of the penalty coefficient. The difference between training labeled samples and training unlabeled samples is that the variation range of the penalty coefficient of labeled samples is larger than that of unlabeled samples. If in the process of CP-Step, pseudo-labels of unlabeled samples are classified incorrectly, call DJ-Step to flip the pseudo-labels, and use incremental and decremental algorithms to make the KKT condition satisfied. We also analyze the time complexity and convergence of ILTSVM. The experimental results show that compared with other incremental or batch learning algorithms, our algorithm is the most effective and fastest method for training TSVM.
Face Detection and Recognition From Distance Based on Deep Learning

Face recognition is an important biometric in video surveillance. However, the conventional algorithms of face recognition are also susceptible to various conditions. The contribution of this chapter is to explore face recognition by using deep learning, including detecting the location of human faces on the given images at various distances and multiple angles. In the distances, the influence of the distance from the camera to a face and the size of the face in the images is explored. There are 500 images collected from various videos as the input and they are applied to train the proposed models. The accuracy of face recognition from the videos excluding training dataset is at 90.18%. The results indicate that this method is able to recognise human faces with partial occlusion and various distances.
Spectral features of Fe and organic carbon in estimating low and moderate concentration of heavy metals in mangrove sediments across different regions and habitat types

Soil or sediment spectral reflectance do not directly reflect information of spectrally featureless heavy metals. With mangrove sediment samples collected in five regions and four habitat types, this study aimed to explore the spectral features related to spectrally active constituents (Fe and organic carbon (OC)) in estimating low and moderate concentrations of four heavy metals (Cr, Ni, Cu and Pb). A total of 27 modeling strategies using XGBoost (extreme gradient boosting) model were compared for estimating each heavy metal: three types of spectral features (Fe, OC and heavy metal) * three spectral transformation methods (Log(1/R), first derivative (FD) and continuous wavelet transform (CWT)) * three variable selection methods (genetic algorithm (GA), successive projection algorithm (SPA) and recursive feature elimination (RFE)). The results showed that the combination of CWT and RFE was recommended for heavy metal estimation, and the ranking of model accuracy was Cu > Pb > Ni > Cr. The optimal XGBoost model was found using spectral features related to Fe for Cr (RVal2(determination coefficient of independent validation) = 0.42, RMSE (root mean square error) = 0.32 and RPIQ (the ratio of performance to interquartile range) = 1.53) and Cu estimation (RVal2 = 0.87, RMSE = 0.19 and RPIQ = 2.20), spectral features related to OC for Ni estimation (RVal2 = 0.63, RMSE = 0.26 and RPIQ = 2.38) and spectral features related to Pb itself for Pb estimation (RVal2 = 0.82, RMSE = 0.23 and RPIQ = 2.59). Moreover, the XGBoost model transferability across different habitat types outperformed that across different regions and data ranges of Fe and OC. We conclude that the modeling strategy using spectral features related to spectrally active constituents has great potentials in quantifying low and moderate concentration of sediment heavy metals, and the samples with high Fe concentration (31.62–37.29 g kg−1) are recommended to improve estimation accuracy of low and moderate concentrations of heavy metals, which could provide sampling strategy development and early warning of heavy metal pollution in mangrove ecosystem.
Single-Objective Particle Swarm Optimization-Based Chaotic Image Encryption Scheme

High security has always been the ultimate goal of image encryption, and the closer the ciphertext image is to the true random number, the higher the security. Aiming at popular chaotic image encryption methods, particle swarm optimization (PSO) is studied to select the parameters and initial values of chaotic systems so that the chaotic sequence has higher entropy. Different from the other PSO-based image encryption methods, the proposed method takes the parameters and initial values of the chaotic system as particles instead of encrypted images, which makes it have lower complexity and therefore easier to be applied in real-time scenarios. To validate the optimization framework, this paper designs a new image encryption scheme. The algorithm mainly includes key selection, chaotic sequence preprocessing, block scrambling, expansion, confusion, and diffusion. The key is selected by PSO and brought into the chaotic map, and the generated chaotic sequence is preprocessed. Based on block theory, a new intrablock and interblock scrambling method is designed, which is combined with image expansion to encrypt the image. Subsequently, the confusion and diffusion framework is used as the last step of the encryption process, including row confusion diffusion and column confusion diffusion, which makes security go a step further. Several experimental tests manifest that the scenario has good encryption performance and higher security compared with some popular image encryption methods.
A hybrid model for automatic modulation classification based on residual neural networks and long short term memory

This paper introduces a deep learning (DL)-based Automatic Modulation Classification (AMC) model. Our model is considered to be a receiver with a modulation classifier that is capable of differentiating ten modulation techniques. The classifier combines the residual neural network (ResNet) and the long short-term memory network (LSTM). The ResNet boosts the accuracy in deep neural networks, and LSTM improves the classifier's performance by passing the time-series previous state information to the current state. This paper demonstrates that the proposed model achieves 92% peak recognition accuracy at 18 dB SNR. It is higher than the ResNet by 11.4%, the CNN network by 4.7%, and the CLDNN network by 2%. Moreover, it delivers more than 90% classification accuracy at SNR above 0 dB. Additionally, it improves the classification accuracy at low SNR by achieving 85.5% accuracy at −2 dB SNR. Furthermore, it advances the recognition accuracy of various modulation recognition methods by more than 98% at SNR above 0 dB.
CFA: Constraint-based Finetuning Approach for Generalized Few-Shot Object Detection

Few-shot object detection (FSOD) seeks to detect novel categories with limited data by leveraging prior knowledge from abundant base data. Generalized few-shot object detection (G-FSOD) aims to tackle FSOD without forgetting previously seen base classes and, thus, accounts for a more realistic scenario, where both classes are encountered during test time. While current FSOD methods suffer from catastrophic forgetting, G-FSOD addresses this limitation yet exhibits a performance drop on novel tasks compared to the state-of-the-art FSOD. In this work, we propose a constraint-based finetuning approach (CFA) to alleviate catastrophic forgetting, while achieving competitive results on the novel task without increasing the model capacity. CFA adapts a continual learning method, namely Average Gradient Episodic Memory (A-GEM) to G-FSOD. Specifically, more constraints on the gradient search strategy are imposed from which a new gradient update rule is derived, allowing for better knowledge exchange between base and novel classes. To evaluate our method, we conduct extensive experiments on MS-COCO and PASCAL-VOC datasets. Our method outperforms current FSOD and G-FSOD approaches on the novel task with minor degeneration on the base task. Moreover, CFA is orthogonal to FSOD approaches and operates as a plug-and-play module without increasing the model capacity or inference time.
Detecting, Tracking and Counting Motorcycle Rider Traffic Violations on Unconstrained Roads

In many Asian countries with unconstrained road traffic conditions, driving violations such as not wearing helmets and triple-riding are a significant source of fatalities involving motorcycles. Identifying and penalizing such riders is vital in curbing road accidents and improving citizens’ safety. With this motivation, we propose an approach for detecting, tracking, and counting motorcycle riding violations in videos taken from a vehicle-mounted dashboard camera. We employ a curriculum learning-based object detector to better tackle challenging scenarios such as occlusions. We introduce a novel trapezium-shaped object boundary representation to increase robustness and tackle the rider-motorcycle association. We also introduce an amodal regressor that generates bounding boxes for the occluded riders. Experimental results on a large-scale unconstrained driving dataset demonstrate the superiority of our approach compared to existing approaches and other ablative variants.
A Parallel Secure Flow Control Framework for Private Data Sharing in Mobile Edge Cloud

Nowadays, the rapid development of edge computing is accelerating the data sharing between cloud computing platforms and mobile users. These data often contain sensitive information, which faces severe leakage risks not only from the semi-trusted cloud servers but also from the malicious senders in the organizations. Fortunately, access control encryption (ACE) has been utilized to secure the data with access control policies, in which a sanitizer (e.g., the edge node) is employed to check all the communications between the sender and receiver, and drop illegal ciphertexts according to the access control policy. However, previous schemes have some limitations in mobile edge cloud, e.g., the sender's attributes are not strictly authenticated in the attribute-based access control policy, or the sanitization time is the bottleneck of fast data sharing. To this end, we introduce PSFlow, a parallel secure flow control framework for private data sharing in mobile edge cloud. First, we propose an attribute-based outsourced ACE (AOACE) scheme, which achieves secure fine-grained data read and write control, and reduces the computational cost of the sender and receiver with outsourced computations in edge nodes. Then, we propose a concrete construction of PSFlow from AOACE, and accelerate the sanitization process with parallel computing. Specifically, PSFlow parallelizes the sanitization operations with a multi-server model in each edge node, and optimizes the sanitization efficiency in each edge server by constructing a shared pool from the attribute universe. The experimental results show that PSFlow is more efficient and practical than previous schemes in mobile edge cloud.
Arabic Text Steganography Based on Deep Learning Methods

Steganography is one of the oldest methods for securely sending and transferring secret information between two people without raising suspicion. Recently, the use of Artificial Intelligence (AI) has become simpler and more widely used. Since the emergence of natural language processing (NLP), building language models using deep learning has become more. Furthermore, because of the importance of concealing secret information in delivered messages, Artificial Intelligence theories along with Natural Language Processing algorithms were employed to conceal secret information within the text cover. The Arabic language was used because of its large number of words, vocabulary, and linguistic meanings, and its most significant feature is Arabic poetry. This study discovered a new way to hide secret data inside newly formulated Arabic poetry based on previous Arabic poetic texts and a database of a number of Arab poets from the ancient and modern eras using Artificial Intelligence and Long Short-Term Memory (LSTM) theories to increase storage capacity by 45 percent. The linguistic accuracy and volume of secret data hidden within the formulated poetry were increased using a Baudot Code algorithm, where the secret data is hidden at the level of letters rather than words, and the linguistic accuracy and volume of secret data hidden within the formulated poetry were increased to eliminate the drawbacks found in previous studies.
RuleCOSI+: Rule extraction for interpreting classification tree ensembles

Despite the advent of novel neural network architectures, tree-based ensemble algorithms such as random forests and gradient boosting machines still prevail in many practical machine learning problems in manufacturing, financial, and medical domains. However, tree ensembles have the limitation that the internal decision mechanisms of complex models are difficult to understand. Therefore, we present a post-hoc interpretation approach for classification tree ensembles. The proposed method, RuleCOSI+, extracts simple rules from tree ensembles by greedily combining and simplifying their base trees. Compared with its previous version, RuleCOSI, this new version can be applied to both bagging (e.g., random forest, RF) and boosting ensembles (e.g., gradient boosting machines, GBM) and run much faster for ensembles with hundreds of trees. To assess the performance and applicability of the method, empirical experiments were conducted using two bagging algorithms and four gradient boosting algorithms over 33 datasets. RuleCOSI+ could generate the best classification rulesets in terms of F-measure together with RuleFit for RF and GBM models of the datasets among five ensemble simplification algorithms, but the rulesets of RuleCOSI+ had, on average, less than half the size of those of RuleFit. Moreover, RuleCOSI+ had the best antecedent uniqueness rate ("uniq") among the five algorithms, and had also ranked high in the number of rules ("Nrules") and the rule reduction rate ("redu"). In addition, the proposed method could reduce generalization errors in the simplified rulesets to obtain, on average, slightly better classification errors than original models of two bagging and three gradient boosting algorithms except CATBoost.
A Region-Based Deep Learning Approach to Automated Retail Checkout

Automating the product checkout process at conventional retail stores is a task poised to have large impacts on society generally speaking. Towards this end, reliable deep learning models that enable automated product counting for fast customer checkout can make this goal a reality. In this work, we propose a novel, region-based deep learning approach to automate product counting using a customized YOLOv5 object detection pipeline and the DeepSORT algorithm. Our results on challenging, real-world test videos demonstrate that our method can generalize its predictions to a sufficient level of accuracy and with a fast enough runtime to warrant deployment to real-world commercial settings. Our proposed method won 4th place in the 2022 AI City Challenge, Track 4, with an F1 score of 0.4400 on experimental validation data.