Reviewerly/exHarmony huggingface dataset:
Global-to-Contextual Shared Semantic Learning for Fine-Grained Vision-Language Alignment

The primary requisites of fine-grained vision-language alignment focus on learning effective features to discriminate fine-grained subcategories and aligning heterogeneous data. This paper proposes a global-to-contextual shared semantic learning for fine-grained vision-language alignment method to address the above challenges. Precisely, to enhance the discrimination of features inside intra-modality, this method extracts the global and contextual vision and language features and carries out features joint learning. Further, this method constructs a shared semantic space, which bridges the semantic correlation of heterogeneous data. Extensive experiments demonstrate the effectiveness of our approach.
GII: A Unified Approach to Representation Learning in Open Set Recognition with Novel Category Discovery

In this paper, we consider the problem of Novel Class Discovery (NCD) in Open Set Recognition (OSR). Given a labeled and an unlabeled set for training, NCD aims to discover the novel categories in the unlabeled set with prior knowledge learned from the labeled set. Existing approaches tackle the NCD problems under a close-set setting, where only the existing categories from the labeled set and the novel categories from the unlabeled set will occur during the inference. This paper considers a more realistic open-set scenario. In the open-set setting, in addition to the existing and novel categories, some unknown categories absent from the training could be present during inference. To address NCD in the open-set scenario, we propose the General Inter-Intra (GII) loss, a unified approach for learning representations from both labeled and unlabeled samples. The proposed approach discovers novel categories in the training set (NCD) meanwhile recognizes the unknown categories (OSR). We evaluate GII with image and graph datasets, and the results indicate that our proposed approach is more effective than other NCD and OSR approaches.
The Optimization and Parallelization of Two-Dimensional Zigzag Scanning on the Matrix

With the expansion of applications, such as image processing, scientific computing, numerical simulation, biomedicine, social network and so on, enormous quantities of data need to be crunched in order to get the valuable parts and discard redundant ones. For those data represented as two-dimensional digital matrix, two alternative schemes by scanning the elements of the matrix in zigzag route have been proposed. Fixed-point mode and navigation mode used in the proposed schemes are introduced. Performance comparison between the two schemes and previous works are analyzed. The experimental results show that our proposed schemes perform well with large scales of matrices. Moreover, the design of parallel programming based on our proposed scheme has been given. Finally, we have discussed the efficiency of parallelization. The speedup we get is from 3.413 to 3.996, which is stably growth with the scales of matrices.
Unsupervised Shape Enhancement and Factorization Machine Network for 3D Face Reconstruction

Existing unsupervised methods are often unable to capture accurate 3D shapes due to the ambiguity of shapes and albedo maps, limiting their applicability to downstream tasks. Therefore, this article proposes an unsupervised shape enhancement and decomposition machine network for 3D facial reconstruction. Specifically, we design a shape enhancement network, further combining global and local features, which can restore more complete and realistic albedo images without introducing additional supervision, so as to obtain higher-quality 3D faces. Secondly, based on the principle of decomposition machines, we design a decomposition module. By decomposing large matrices, the network learns to infer better results, while reducing the number of network parameters further improving the accuracy of our model. Extensive experiments on BFM and CelebA data demonstrate the effectiveness of our methods.
Au-Cu Resources in Some Mines from Antiquity in the South Gabal Um Monqul and Gabal Al Kharaza Prospects, North Eastern Desert, Egypt

Since Antiquity, sustainable resources of gold and copper have been mined at two prominent prospects in the north Eastern Desert of Egypt, namely the south Gabel Um Monqul (SGUM) and Gabal Al Kharaza (GKZ). Mineralization is hosted by Neoproterozoic shield rocks represented by dacite and monzo- to syenogranite at the SGUM prospect whereas they are diorite, granodiorite, and quartz feldspar porphyry at the GKZ prospect. These hosts have been emplaced in an island arc environment from calc-alkaline magmas with a peraluminous to metaluminous signature. They are hydrothermally altered including albitization, sericitization, silicification, epidotization, and chloritization. The Au and Cu mineralizations are confined to shear zones that lately filled with auriferous quartz veins adjacent to mineralized alteration zones. In the GKZ prospect, the old trenches trend mainly in a NW–SE direction whereas it is NE–SW and NW–SE in the SGUM prospect. Evidence of shearing ranges from megascopic conjugate fractures and shear planes in the outcrops to microscopic sheared and crumbled Au-Cu ore assemblages dominated by Fe-Cu sulfides, specularite, and barite. Microscopic investigation suggests that the formation of specularite is due to the shearing of early existing magnetite. The ore textures and paragenetic sequence indicate that pyrite in the alteration zones is oxidized, leading to the liberation of gold up to 3.3 g/t. The formulae of the analyzed electrum lie in the range Au74.5-76.8 Ag22.2-24.5. Integration of the field, geochemistry, and mineral chemistry data, combined with the gold fire assay data prove the presence of sustainable amounts of disseminated Au and Cu, not only in the mineralized quartz veins, but also in the alteration zones. Data materialized in our paper show similarities in the style of mineralization at the SGUM and the GKZ prospects with iron oxide-copper-gold (IOCG) deposits elsewhere in the Arabian-Nubian Shield (ANS) and other world examples.
Damage Detection in a Polymer Matrix Composite from 4D Displacement Field Measurements

Standard Digital Volume Correlation (DVC) approaches enable quantitative analyses of specimen deformation to be performed by measuring displacement fields between discrete states. Such frameworks are thus limited by the number of scans (due to acquisition duration). Considering only one projection per loading step, Projection-based Digital Volume Correlation (P-DVC) allows 4D (i.e., space and time) full-field measurements to be carried out over entire loading histories. The sought displacement field is decomposed over a basis of separated variables, namely, temporal and spatial modes. In the present work, the spatial modes are constructed via scan-wise DVC, and only the temporal amplitudes are sought via P-DVC. The proposed method is applied to a glass fiber mat reinforced polymer specimen containing a machined notch, subjected to in situ cyclic tension and imaged via X-ray Computed Tomography. The P-DVC enhanced DVC method employed herein enables for the quantification of damage growth over the entire loading history up to failure.
Intelligent Online Inspection of the Paste Quality of Prebaked Carbon Anodes Using an Anomaly Detection Algorithm

Prebaked carbon anodes are a critical consumable in the aluminum electrolysis industry. Prebaked carbon anode paste is the intermediate product of the prebaked carbon anode, and its quality significantly impacts the prebaked carbon anode. Therefore, inspecting the quality of the prebaked carbon anode paste is essential. Currently, the quality inspection of the paste still relies on laboratory analysis or manual experience. A laboratory inspection cannot obtain results in real time, while manual inspection poses potential risks. To address these issues, an online intelligent inspection method for prebaked carbon anode paste based on an anomaly detection algorithm was proposed. Firstly, we acquired the temperature of the paste and the power of the kneading motor. Secondly, we transformed these time-series data into images using the Gramian Angular Field (GAF) technique and joined them to create the paste anomaly detection dataset. Thirdly, we trained a matched anomaly detection model based on the PatchCore algorithm. Finally, we compared two advanced models: HaloAE and TSRD. PatchCore performs best on our dataset with an AUC-ROC score of 0.9943, followed by HaloAE (0.9906) and TSRD (0.9811). Our proposed method enables on-time intelligent inspection of prebaked carbon anode paste quality. This eliminates the need for manual inspection, reduces labor requirements, and ensures worker safety.
Errors in Pronouncing English in the English Department Students

The aim of this study was to find out students’ errors in pronouncing vowel sounds which consisted of twelve sounds. The researcher used fourth-semester students of English Department at the University of Bengkulu. The method used in this research was descriptive qualitative because the researcher described about the students’ errors. Four students taken from three classes of English Department in academic year 2021/2022 WERE the sample used in this study. To collect the data, the researcher used a pronunciation test as the instrument. In analyzing the data, the researcher transcribed the recording in the form of phonetic transcription. As a result, the researcher found that students made errors in all of the vowel sounds. All of the students made error in pronouncing vocabularies with vowel sound /ɔ:/ and /ʊ/. Those errors are classified into three kinds of errors namely vowel length error, different English vowel sound system from Indonesian Vowel and students’ Wrong Pronunciation Acquisition. First, the students cannot differentiate the vowel length such as vowel /iː/ into vowel /ɪ/ and vice versa, vowel /u:/ to vowel /ʊ/, vowel /ɜː/ into vowel /e/ and vowel sound /Ʌ/ into /ɑ:/. Second, the students substituted the vowel /æ/ into vowel /e/ and vowel sound /ә/ into /e/ or vice versa because there is no vowel sounds such as /æ/ and /ә/ in Indonesian. Last, the students substituted the vowel /ɔː/ into /ɑ:/ and vowel /ʊ/ into vowel /ə/ where they were actually different but the students considered that it was true.
Decision Support System for Extreme Poverty BLT Recipients Combining the ROC and WASPAS Methods

The Covid-19 pandemic has begun to be under control and the basis for the Village Fund BLT distribution has been adjusted. BLT Dana Desa aims to increase the income of extremely poor families in the village. In determining and determining prospective Beneficiary Families (KPM), each village will be guided by the data for the Acceleration of Extreme Poverty Elimination (PPKE) provided by the central government through the local government which will later be verified by the village government. Therefore, a Decision Support System is needed to find out who really deserves assistance, so that the allocation can be right on target according to predetermined criteria. In this study the results of this study show that the proposed model can be used well in conducting the selection process for laboratory assistant admissions. In this research, the use of ROC is able to provide appropriate criteria weights based on the level of importance of the criteria from the decision maker. Meanwhile, the use of the WASPAS method is able to produce decisions in the form of the best alternatives that can be used to help decision makers. From the calculation process that has been done, it can be concluded that Sunardi got the highest score, namely 0.7276 and Muksin got the lowest score, namely 0.5491. The existence of this system can make it easier for the government, especially villages, to identify beneficiaries and minimize errors in selecting beneficiaries.
Gated Multi-modal Fusion with Cross-modal Contrastive Learning for Video Question Answering

Video Question Answering (VideoQA) is a challenging task that requires the model to understand the complex nature of video data and the variety of questions that can be asked about them. Existing approaches often suffer from the problem of ambiguous answer candidates with low relevance to the visual and auditory part of the video, which limits the performance of VideoQA systems. In this paper, we introduce a novel approach that leverages multi-modal fusion and cross-modal contrastive learning to utilize multi-modal information and enhance the relevance of answer candidates in VideoQA. First, we introduce a gated multi-modal fusion network that learns to combine different modalities such as visual and speech based on their relevance to the question to enrich the representations of video and improve the accuracy of finding the correct answer. Second, we introduce cross-modal contrastive learning to increase the similarity between positive example pairs (i.e., correct answers and corresponding video clips) while decreasing the similarity between negative example pairs (i.e., incorrect answers and unpaired video clips). Specifically, we use three-way contrastive learning between answer and video frame, answer and audio, answer and cross-modal features. Our proposed approach is evaluated on two benchmark audio-aware VideoQA datasets, including AVQA and Music-AVQA, and compared to several state-of-the-art methods. The results show that our approach significantly improves the performance of VideoQA, achieving new state-of-the-art results on these benchmarks.
PV Potential Assessment Platform

Collective self-consumption and renewable energy communities (RECs) have gained global attention due to increasing integration of distributed energy resources (DERs) into the power system and rising concerns of consumers in the electricity market. The determination of the renewable generation potential in a zone becomes a relevant input for planning purposes. This paper presents the platform being developed by the research team to automatically assess the solar power and yearly energy that can be produced in any given area. The platform combines manual selection with geographic information system (GIS) based methods and incorporates satellite solar radiation data. A case study applied at Las Palmas Port in Gran Canaria (Canary Islands, Spain) resulted on the definition of 342,020 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of suitable surfaces for PV panels that could be used to generate 70.81 MWp, capable of producing 113.351 GWh per year.
An Efficient Image Watermarking Method Based on Discrete Cosine Transform

The security of information transmission is a field of extensive research due to its importance nowadays. Watermarking is one of the methods used to protect the originality of the carrier images/objects. It is also considered to be a method for transmitting certain information and an effective way of tracing information. However, despite efforts to protect the data through watermarking, maintaining the proper quality of both the watermark itself and the carrier image remains a significant challenge. It is therefore essential to develop watermarking methods that are capable of achieving acceptable results. In this study, we proposed a watermarking method based on Discrete Cosine Transform (DCT) that is capable of embedding and extracting the watermark without compromising the quality of the original watermark. The success of this system derives from the strategic application of the JPEG quantization table, specifically the luminance table. The proposed method was able to achieve satisfying results (NC=0.998, and PSNR=41.4099) despite the fact that the image was subjected to JPEG compression.
No Matter Small or Big Lip Motion: DeepFake Detection with Regularized Feature Learning on Semantic Information

The use of DeepFake technologies to create hyper-realistic faces has sparked serious security concerns. Recent advances on DeepFake detection showed promise on algorithm generalization to unseen manipulation methods by identifying high-level semantic irregularities. However, the extracted features are not always robust, as the sample variations such as different motion magnitudes can easily degrade the feature-vector representations of their semantic information. In this work, we propose DTNet, a novel deep method that further regularizes feature learning toward more robust DeepFake Detection. To be specific, the proposed DTNet contains Deviation Regularization that penalizes samples with deviated motion magnitudes in the loss function, and Temporal Continuity Preservation, which helps keep and learn patterns of temporal continuity in feature space regardless of motion magnitudes. Experimental results show that our method effectively mitigates the impact of motion magnitudes on feature vectors, thereby improving the generalization ability.
An image denoising method based on the nonlinear Schrödinger equation and spectral subband decomposition

Most denoising methods inevitably smooth the image while denoising, which makes it difficult to effectively maintain the structural information and detail information of the original noise-free image. Stochastic resonance can directly convert the noise energy into signal energy, and denoising by stochastic resonance retains the signal energy. Therefore, this paper presents an image denoising method based on the nonlinear Schrödinger equation. First, by analyzing the dynamic stochastic resonance effect in a nonlinear optical system, the nonlinear Schrödinger equation is derived to describe the signal propagation in the system, and the equation is extended to construct a two-dimensional image denoising model. Then, we propose an image denoising algorithm based on image spectral subband decomposition. The algorithm decomposes the image spectrum into different frequency domain subbands and uses the Schrödinger denoising model to denoise the high-frequency subbands containing the rich structure and detailed information of the image. A series of comparative experiments show that the proposed method can significantly improve the image signal-to-noise ratio while effectively restoring image details and has better performance than other typical methods in the case of high-intensity noise.
Editorial for Special Issue “Novel Methods and Applications for Mineral Exploration, Volume II”

As already exemplified by the highly successful first volume of this Special Issue, both the theoretical understanding of mineral exploration and its practical application in the field are undergoing a profound and significant transformation [...]
A no-reference quality assessment metric for dynamic 3D digital human

Dynamic Digital Humans (DDHs) refer to 3D digital models that are animated using predefined motions. However, these models are susceptible to noise and shift distortions during the generation process, as well as compression distortions during transmission and communication. It is crucial to evaluate the perceptual quality of DDHs in order to address these challenges. Typically, DDHs are presented as 2D rendered animation videos, making it reasonable to adapt video quality assessment (VQA) methods for DDH quality assessment (DDH-QA) tasks. Nevertheless, VQA methods heavily rely on viewpoints and exhibit limited sensitivity to geometry-based distortions. Consequently, this paper introduces a novel no-reference (NR) quality assessment metric specifically tailored for the DDH-QA challenge. In this proposed method, the geometry characteristics of DDHs are characterized by computing statistical parameters estimated from the distributions of their geometry attributes. Additionally, spatial and temporal features are extracted from the rendered videos. Subsequently, all relevant features are integrated and regressed into quality values. Through comprehensive experiments conducted on the DDH-QA database, the results demonstrate that the proposed method achieves state-of-the-art performance in assessing the quality of DDHs.
Voting Classifier for The Interactive Design with Deep Learning for Scene Theory

Tool products play a pivotal role in assisting individuals in various domains, ranging from professional work to everyday tasks. The success of these tools is not solely determined by their functionality but also by the quality of user experience they offer. Designing tool products that effectively engage users, enhance their productivity, and provide a seamless interaction experience has become a critical focus for researchers and practitioners in the field of interaction design. Scene theory proposes that individuals perceive and interpret their surroundings as dynamic "scenes," wherein environmental and situational factors influence their cognitive processes and behavior. This research paper presented a novel approach to the interaction design of tool products by integrating scene theory, flow experience, the Moth Flame optimization (MFO), cooperative game theory (CGT), and voting deep learning. Tool products play a vital role in various domains, and their interaction design significantly influences user satisfaction and task performance. Building upon the principles of scene theory and flow experience, this study proposes an innovative framework that considers the contextual factors and aims to create a seamless and enjoyable user experience. The MFO algorithm, inspired by the behavior of moth flame, is employed to optimize the design parameters and enhance the efficiency of the interaction design process. Furthermore, CGT is integrated to model cooperative relationships between users and tool products, fostering collaborative and engaging experiences. Voting deep learning is employed to analyze user feedback and preferences, enabling personalized and adaptive design recommendations. With the proposed CGT, this paper investigates the impact of the proposed approach on user engagement, task efficiency, and overall satisfaction. The findings contribute to the field of interaction design by providing practical insights for creating tool products that align with users' cognitive processes, environmental constraints, flow-inducing experiences, and cooperative dynamics.