Reviewerly/exHarmony huggingface dataset:
Development of a Solar-Tracking System for Horizontal Single-Axis PV Arrays Using Spatial Projection Analysis

Uniaxial trackers are widely employed as the frame for solar photovoltaic (PV) panel installation. However, when used in sloping terrain scenarios such as mountain and hill regions, it is essential to apply a solar-tracking strategy with the sloping factors considered, to eliminate the shading effects between arrays and reduce the electricity production loss due to terrain changes. Based on a uniaxial tracker on the sloping terrain of a PV farm located in Ningxia, this study established a uniaxial solar-tracking strategy for sloping terrain by integrating a spatial projection model with a dynamic shadow assessment method. In the proposed strategy, the optimal tilt angle of the PV array and related desirable adjustment are identified taking into consideration major parameters such as the shadow area ratio S and the average solar irradiance intensity G. A tool underpinned by Matlab Simulink has also been developed to realize the proposed solar-tracking strategy. With the input of a simulated ramp signal β and the dynamically changed time parameters, the tracking angle of PV arrays over the simulated duration is accurately predicted, followed by a series of experimental validations conducted on the winter solstice and a typical sunny day (15 September). Moreover, the study also explored the terrain impacts on solar tracking by comparing the sloping terrain and flat terrain applications. The analytic and experimental results indicate that (a) the maximum value of the G(β) function could serve as the input to identify the optimal tracking angle; (b) the application of the flat terrain tracking (FTT) strategy in sloping terrain would result in a reduction of average solar irradiance intensity harvested by the PV arrays with varying degrees; (c) in the context of an east–west −7° sloping terrain, compared with the FTT strategy, the sloping terrain tracking (STT) strategy enabled anti-shading tracking, and then increased the daily PV electricity yield by 0.094 kWh/kWp, which is around 1.48% of the daily energy production; (d) given a measurement with annual scale, the STT strategy could cause a 1.26% increase in the energy harvesting with a flat uniaxial PV array on a −7° slope terrain, achieving an annual increase of 25.16 kWh/kWp. The experimental comparative analysis validated the precision of the proposed solar-tracking model, which has far-reaching significance for achieving automatic solar-tracking of PV modules, as well as improving the capacity and efficiency of PV systems.
Accelerating the Response of Self-Driving Control by Using Rapid Object Detection and Steering Angle Prediction

A vision-based autonomous driving system can usually fuse information about object detection and steering angle prediction for safe self-driving through real-time recognition of the environment around the car. If an autonomous driving system cannot respond fast to driving control appropriately, it will cause high-risk problems with regard to severe car accidents from self-driving. Therefore, this study introduced GhostConv to the YOLOv4-tiny model for rapid object detection, denoted LW-YOLOv4-tiny, and the ResNet18 model for rapid steering angle prediction LW-ResNet18. As per the results, LW-YOLOv4-tiny can achieve the highest execution speed by frames per second, 56.1, and LW-ResNet18 can obtain the lowest prediction loss by mean-square error, 0.0683. Compared with other integrations, the proposed approach can achieve the best performance indicator, 2.4658, showing the fastest response to driving control in self-driving.
General-use unsupervised keyword extraction model for keyword analysis

Keyword extraction is the foundation for solving various text mining tasks. However, the literature heavily relies on statistical, linguistic feature-based, or graph-based metrics to gauge corpus-representative keywords, the process of which is sensitive to preprocessing and stopword selection. In this paper, we propose a general-use keyword extraction model designed to work with document groups of various sizes, domains, and readability, as well as the existence of keyword labels. To extract a better selection of keywords, we employ a simple logistic regression model with the least absolute shrinkage and selection operator regularization (Tibshirani, 1996). The classification-based structure of our approach ensures learning words that distinctively characterize the given document group against the comparison groups, enhancing the representativeness of the extracted keywords. Furthermore, our model repeatedly modifies coefficients as it learns the document label classifiers, rather than relying directly on the term frequencies, reducing the model's sensitivity to words of very high and very low frequencies. We test our model's performance against numerous classic keyword extraction frameworks as baseline models using online customer reviews, news articles, and patent documentation. The results indicate that our proposed method has robust performance in terms of representability and distinctiveness across document groups with varying sizes, number of class labels, levels of readability, and domain. Additionally, we show that our model beats baseline models even when applied to documents without class labels compared with the baseline models. Given its generalizability and simplicity, we believe that our proposed model may serve as an easy-to-use, yet a powerful, general-use tool for keyword extraction, especially when working with various groups of documents from different domains.
Conditional Independence Induced Unsupervised Domain Adaptation

Learning domain-adaptive features is important to tackle the dataset bias problem, where data distributions in the labeled source domain and the unlabeled target domain can be different. The critical issue is to identify and then reduce the redundant information including class-irrelevant and domain-specific features. In this paper, a conditional independence induced unsupervised domain adaptation (CIDA) method is proposed to tackle the challenges. It aims to find the low-dimensional and transferable feature representation of each observation, namely the latent variable in the domain-adaptive subspace. Technically, two mutual information terms are optimized at the same time. One is the mutual information between the latent variable and the class label, and the other is the mutual information between the latent variable and the domain label. Note that the key module can be approximately reformulated as a conditional independence/dependence based optimization problem, and thus, it has a probabilistic interpretation with the Gaussian process. Temporary labels of the target samples and the model parameters are alternatively optimized. The objective function can be incorporated with deep network architectures, and the algorithm is implemented iteratively in an end-to-end manner. Extensive experiments are conducted on several benchmark datasets, and the results show effectiveness of CIDA.
Quantum computing on nucleic acid research: Approaching towards next-generation computing

Recently, IBM announced a new quantum computer with a 400-plus qubit processor, which helps the realm of quantum computing to attain the next step. The newly developed IBM computer with a 433 qubit processor is called “Osprey.” It is one of the breakthroughs in qubit technology, with 400-plus qubits (Figure 1A). This quantum computer will help in the advancements in quantum computation.1 The example pointed out the developmental effort of quantum computers by the industry. The industry, academia, and scientific communities are putting tremendous efforts into developing next-generation computing systems, quantum computers, to resolve the world’s most challenging problems.
Differential Dataset Cartography: Explainable Artificial Intelligence in Comparative Personalized Sentiment Analysis

Data Maps is an interesting method of graphical representation of datasets, which allows observing the model's behaviour for individual instances in the learning process (training dynamics). The method groups elements of a dataset into easy-to-learn, ambiguous, and hard-to-learn. In this article, we present an extension of this method, Differential Data Maps, which allows you to visually compare different models trained on the same dataset or analyse the effect of selected features on model behaviour. We show an example application of this visualization method to explain the differences between the three personalized deep neural model architectures from the literature and the HumAnn model we developed. The advantage of the proposed HumAnn is that there is no need for further learning for a new user in the system, in contrast to known personalized methods relying on user embedding. All models were tested on the sentiment analysis task. Three datasets that differ in the type of human context were used: user-annotator, user-author, and user-author-annotator. Our results show that with the new explainable AI method, it is possible to pose new hypotheses explaining differences in the quality of model performance, both at the level of features in the datasets and differences in model architectures.
Towards a Theory of Postdigital Parity

In conducting postdigital research, the relationship between the predigital and the digital can be perplexing. One way to forge continuity between the two involves formulating a flexible principle of parity or functional equality—what I have elsewhere called ‘postdigitalism’s parity thesis’. According to this seminal formulation, the digital and predigital should be treated as relatively equal, except when there is a good reason to prioritize one over the other. However, the principle—at least as it is currently formulated—lacks sufficient theoretical grounding to serve as anything more than a rule-of-thumb for conducting postdigital inquiry. The point of the chapter is to rectify this shortcoming, suggesting one or more theories of postdigital parity. The meta-rationale for this project is to offer a demonstration of how to theoretically ground ancillary concepts in postdigital research. Grounding a concept in a robust theory assists the researcher in operationalizing related variables and variable relations, thereby making them more concrete and measurable. Clarifying this relationship holds the promise of offering a clearer direction to postdigital inquiry. Out of the survey of recent postdigitalism literature emerges evidence of a workable parity principle.
Hypotheses Tree Building for One-Shot Temporal Sentence Localization

Given an untrimmed video, temporal sentence localization (TSL) aims to localize a specific segment according to a given sentence query. Though respectable works have made decent achievements in this task, they severely rely on dense video frame annotations, which require a tremendous amount of human effort to collect. In this paper, we target another more practical and challenging setting: one-shot temporal sentence localization (one-shot TSL), which learns to retrieve the query information among the entire video with only one annotated frame. Particularly, we propose an effective and novel tree-structure baseline for one-shot TSL, called Multiple Hypotheses Segment Tree (MHST), to capture the query-aware discriminative frame-wise information under the insufficient annotations. Each video frame is taken as the leaf-node, and the adjacent frames sharing the same visual-linguistic semantics will be merged into the upper non-leaf node for tree building. At last, each root node is an individual segment hypothesis containing the consecutive frames of its leaf-nodes. During the tree construction, we also introduce a pruning strategy to eliminate the interference of query-irrelevant nodes. With our designed self-supervised loss functions, our MHST is able to generate high-quality segment hypotheses for ranking and selection with the query. Experiments on two challenging datasets demonstrate that MHST achieves competitive performance compared to existing methods.
A Survey on Model Compression and Acceleration for Pretrained Language Models

Despite achieving state-of-the-art performance on many NLP tasks, the high energy cost and long inference delay prevent Transformer-based pretrained language models (PLMs) from seeing broader adoption including for edge and mobile computing. Efficient NLP research aims to comprehensively consider computation, time and carbon emission for the entire life-cycle of NLP, including data preparation, model training and inference. In this survey, we focus on the inference stage and review the current state of model compression and acceleration for pretrained language models, including benchmarks, metrics and methodology.
Revisiting ‘revisiting supervised methods for effort‐aware cross‐project defect prediction’

Abstract Effort‐aware cross‐project defect prediction (EACPDP), which uses cross‐project software modules to build a model to rank within‐project software modules based on the defect density, has been suggested to allocate limited testing resource efficiently. Recently, Ni et al. proposed an EACPDP method called EASC, which used all cross‐project modules to train a model without considering the data distribution difference between cross‐project and within‐project data. In addition, Ni et al. employed the different defect density calculation strategies when comparing EASC and baseline methods. To explore the effective defect density calculation strategies and methods on EACPDP, the authors compare four data filtering methods and five transfer learning methods with EASC using four commonly used defect density calculation strategies. The authors use three classification evaluation metrics and seven effort‐aware metrics to assess the performance of methods on 11 PROMISE datasets comprehensively. The results show that (1) The classification before sorting (CBS+) defect density calculation strategy achieves the best overall performance. (2) Using balanced distribution adaption (BDA) and joint distribution adaptation (JDA) with the K‐nearest neighbour classifier to build the EACPDP model can find 15% and 14.3% more defective modules and 11.6% and 8.9% more defects while achieving the acceptable initial false alarms (IFA). (3) Better comprehensive classification performance of the methods can bring better EACPDP performance to some extent. (4) A flexible adjustment of the defect threshold λ of the CBS+ strategy contribute to different goals. In summary, the authors recommend researchers and practitioners use to BDA and JDA with the CBS+ strategy to build the EACPDP model.
Comprehensive Evaluation of Publication and Citation Metrics for Quantifying Scholarly Influence

Ranking of researchers based on their scientific impact in a scientific community is indeed a very crucial task. However, identifying the researchers ranking helps the scientific community in various decisions such as awarding scholarships, selection for tenure, awarding achievements, giving promotions, etc. In literature numerous parameters have been proposed for the ranking of researchers, such as publication count, citation count, coauthor count, h-index, and its extensions. The current state-of-the-art research delineates that no such universally accepted parameter exists which can identify the most influential researchers. Therefore, it is necessary to determine an optimal parameter that can effectively rank authors. Furthermore, to identify the best parameter, few of the researchers conducted evaluative surveys as reflected in the literature. In these evaluative surveys, the researchers utilized a limited number of indices on the small and imbalanced datasets, followed by fictional cases and scenarios, this has made it challenging to ascertain the relative importance and impact of each parameter in comparison to the others. This research evaluates the h-index and its thirty-two variants, which are based on the number of publications and citation count category used for ranking the authors. We have collected data from 1050 researchers working in the mathematical domain for our experimental purposes. For the benchmark dataset, we have collected the awardees’ data of the last two decades of four different societies belonging to mathematical domain. First and foremost, we have computed the correlations among the obtained values of the indices to assess their similarities and differences to evaluate indices. The result revealed that there is a high degree of correlation observed among h-index and it’s twenty-four different variants. However, some of the indices represented weak correlation, signifying that their rankings are highly dissimilar to those of other indices. Secondly, the position of awardees is checked in the top 10, top 50, and top 100 return records based on the ranking list of each index. The outcome of the last step divulges that A index, E index, H core citation, H2 lower index, K index, M index, and woginger index retrieved almost 80% of awardees in top 10% ranked list. Further, the analysis revealed that most of the winners (awards) were in the top tier, belonging to IMS, LMS, and AMS society returned by hg index, g index, k index, etc. indicating a relationship between the stated societies and the indices.
Research on Underwater Small Target Detection Algorithm Based on Improved YOLOv7

Target detection research has always been difficult when it comes to small target detection in underwater situations.To address the issues of a high miss detection rate and poor underwater scene recognition in underwater small target detection tasks, an improved underwater small target detection technique utilizing YOLOv7 is proposed.To achieve the accuracy rate while considering the high detection speed, the YOLOv7 network is used as the basic network.The network concentrates more crucial feature information of small targets to increase detection accuracy while reducing model complexity by merging the SENet attention mechanism, enhancing the FPN network topology, and incorporating the EIoU loss function.Through simulation tests, the mAP, P, and R metrics are confirmed on the test set and contrasted with other conventional target detection techniques.The outcomes demonstrate that the enhanced algorithm outperforms competing networks and successfully raises detection accuracy on the test set.
Fine-Grained Retrieval Prompt Tuning

Fine-grained object retrieval aims to learn discriminative representation to retrieve visually similar objects. However, existing top-performing works usually impose pairwise similarities on the semantic embedding spaces or design a localization sub-network to continually fine-tune the entire model in limited data scenarios, thus resulting in convergence to suboptimal solutions. In this paper, we develop Fine-grained Retrieval Prompt Tuning (FRPT), which steers a frozen pre-trained model to perform the fine-grained retrieval task from the perspectives of sample prompting and feature adaptation. Specifically, FRPT only needs to learn fewer parameters in the prompt and adaptation instead of fine-tuning the entire model, thus solving the issue of convergence to suboptimal solutions caused by fine-tuning the entire model. Technically, a discriminative perturbation prompt (DPP) is introduced and deemed as a sample prompting process, which amplifies and even exaggerates some discriminative elements contributing to category prediction via a content-aware inhomogeneous sampling operation. In this way, DPP can make the fine-grained retrieval task aided by the perturbation prompts close to the solved task during the original pre-training. Thereby, it preserves the generalization and discrimination of representation extracted from input samples. Besides, a category-specific awareness head is proposed and regarded as feature adaptation, which removes the species discrepancies in features extracted by the pre-trained model using category-guided instance normalization. And thus, it makes the optimized features only include the discrepancies among subcategories. Extensive experiments demonstrate that our FRPT with fewer learnable parameters achieves the state-of-the-art performance on three widely-used fine-grained datasets.
Geological Structures Controlling Au/Ba Mineralization from Aeromagnetic Data: Harrat ad Danun Area, Saudi Arabia

Positive and negative magnetic anomalies occupied the total aeromagnetic (TM) map of the Harrat ad Danun area, Saudi Arabia. Reduction to the pole (RTP) maps display the range of magnetic values (−312.4 to 209.4 nT) that vary in shape, size, and magnitude. These anomalies generally follow the NNW–SSE (Red Sea axis trend), NE–SW, and NNE–SSW trends. The NNW-SSE linear negative and positive magnetic anomalies could be brought on by buried faults, shear zones, or subsurface dikes. In the central part, the position of Au and Ba mineralization was connected to this trend. It is concluded that the principal structures are represented by the NNW–SSE, NE–SW, and NNE–SSW tendencies. Based on gridded RTP magnetic data, the 2-D power spectrum was computed and revealed the frequency of the near-surface and deep magnetic components. It is believed that the depths of the shallow and deep magnetic sources are typically 80 m and 570 m, respectively. Additional negative and positive magnetic anomalies with varied amplitudes and frequencies, trending in the NNW–SSE, ENE–WSW, and NE–SW directions, are seen when the high-pass and low-pass maps are closely examined. Many faults in various directions cut into these anomalies. The occurrence of negative linear magnetic anomalies (−36.6 nT to −137.3 nT) at this depth (80 m) is also confirmed by this map. The TDR filter and the Euler deconvolution method were used to identify the horizontal variations in magnetic susceptibility as well as the source position and depth of magnetic sources. The linear clustering rings are thought to be caused by contacts or faults with depths between 1 m to 474 m that are oriented WNW–ESE, NNE–SSW, and NNW–SSE. These faults or contacts are thought to be particularly prominent in the western, eastern, southern, northern, and central zones. The majority of felsic and mafic dikes are found to be connected to subsurface structures, showing that three structural trends—WNW–ESE, NNE–SSW, and NNW–SSE—affect the studied area. This demonstrates that important features and shear zones control the majority of Saudi Arabia’s gold deposits. A negative magnetic anomaly that is centered in the area, trending NNW–SSE and crossing the NNE–SSW fault, is connected to the plotted gold and barite mineralization in the study area. This may imply that these two tendencies are responsible for mineralization. This result raises the possibility of mineralization in the NNW negative magnetic feature located in the western part of the area. The occurrence of gold and barite was significantly impacted by the NNW–SSE and NNE–SSW structural lineaments.