Competitive Benchmarking Research Articles

A Study on the Impact of Wage and Salary Management on the Performance of IT Employees in Hyderabad

This research endeavors to investigate the relationship between wage and salary management practices and the performance levels of Information Technology (IT) employees in the vibrant city of Hyderabad. As compensation is a critical factor influencing employee motivation and productivity, understanding its impact on the performance of IT professionals becomes crucial for organizations operating in the technology sector. Employing a mixed-methods approach, combining surveys and interviews, the study aims to examine the various aspects of wage and salary management, including fairness, transparency, and competitive benchmarks, and their influence on employee performance. Key objectives include assessing the correlation between compensation satisfaction and job performance, identifying the factors that contribute to employee perceptions of fairness in wage and salary structures, and exploring the role of non-monetary incentives in enhancing overall job satisfaction and productivity. Additionally, the research aims to provide insights into the preferences and expectations of IT employees regarding their compensation packages. The findings of this study are anticipated to contribute valuable insights for IT organizations in Hyderabad, helping them optimize wage and salary management practices to attract, retain, and motivate high-performing talent.

Improving Medium-Range Ensemble Weather Forecasts with Hierarchical Ensemble Transformers

Abstract Statistical postprocessing of global ensemble weather forecasts is revisited by leveraging recent developments in machine learning. Verification of past forecasts is exploited to learn systematic deficiencies of numerical weather predictions in order to boost postprocessed forecast performance. Here, we introduce postprocessing of ensembles with transformers (PoET), a postprocessing approach based on hierarchical transformers. PoET has two major characteristics: 1) the postprocessing is applied directly to the ensemble members rather than to a predictive distribution or a functional of it, and 2) the method is ensemble-size agnostic in the sense that the number of ensemble members in training and inference mode can differ. The PoET output is a set of calibrated members that has the same size as the original ensemble but with improved reliability. Performance assessments show that PoET can bring up to 20% improvement in skill globally for 2-m temperature and 2% for precipitation forecasts and outperforms the simpler statistical member-by-member method, used here as a competitive benchmark. PoET is also applied to the ENS-10 benchmark dataset for ensemble postprocessing and provides better results when compared to other deep learning solutions that are evaluated for most parameters. Furthermore, because each ensemble member is calibrated separately, downstream applications should directly benefit from the improvement made on the ensemble forecast with postprocessing.

Fine-grained Recognition with Learnable Semantic Data Augmentation.

Fine-grained image recognition is a longstanding computer vision challenge that focuses on differentiating objects belonging to multiple subordinate categories within the same meta-category. Since images belonging to the same meta-category usually share similar visual appearances, mining discriminative visual cues is the key to distinguishing fine-grained categories. Although commonly used image-level data augmentation techniques have achieved great success in generic image classification problems, they are rarely applied in fine-grained scenarios, because their random editing-region behavior is prone to destroy the discriminative visual cues residing in the subtle regions. In this paper, we propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Specifically, we produce diversified augmented samples by translating image features along semantically meaningful directions. The semantic directions are estimated with a covariance prediction network, which predicts a sample-wise covariance matrix to adapt to the large intra-class variation inherent in fine-grained images. Furthermore, the covariance prediction network is jointly optimized with the classification network in a meta-learning manner to alleviate the degenerate solution problem. Experiments on four competitive fine-grained recognition benchmarks (CUB-200-2011, Stanford Cars, FGVC Aircrafts, NABirds) demonstrate that our method significantly improves the generalization performance on several popular classification networks (e.g., ResNets, DenseNets, EfficientNets, RegNets and ViT). Combined with a recently proposed method, our semantic data augmentation approach achieves state-of-the-art performance on the CUB-200-2011 dataset. The source code will be released.

Measurements With A Quantum Vision Transformer: A Naive Approach

In mainstream machine learning, transformers are gaining widespread usage. As Vision Transformers rise in popularity in computer vision, they now aim to tackle a wide variety of machine learning applications. In particular, transformers for High Energy Physics (HEP) experiments continue to be investigated for tasks including jet tagging, particle reconstruction, and pile-up mitigation. An improved Quantum Vision Transformer (QViT) with a quantum-enhanced self-attention mechanism is introduced and discussed. A shallow circuit is proposed for each component of self-attention to leverage current Noisy Intermediate Scale Quantum (NISQ) devices. Variations of the hybrid architecture/model are explored and analyzed. The results demonstrate a successful proof of concept for the QViT, and establish a competitive performance benchmark for the proposed design and implementation. The findings also provide strong motivation to experiment with different architectures, hyperparameters, and datasets, setting the stage for implementation in HEP environments where transformers are increasingly used in state of the art machine learning solutions.

SASAN: Shape-Adaptive Set Abstraction Network for Point-Voxel 3D Object Detection.

Point-voxel 3D object detectors have achieved impressive performance in complex traffic scenes. However, they utilize the 3D sparse convolution (spconv) layers with fixed receptive fields, such as voxel-based detectors, and inherit the fixed sphere radius from point-based methods for generating the features of keypoints, which make them weak in adaptively modeling various geometrical deformations and sizes of real objects. To tackle this issue, we propose a shape-adaptive set abstraction network (SASAN) for point-voxel 3D object detection. First, the proposal and offset generation module is adopted to learn the coordinates and confidences of 3D proposals and shape-adaptive offsets of the certain number of offset points for each voxel. Meanwhile, an extra offset supervision task is employed to guide the learning of shifting values of offset points, aiming at motivating the predicted offsets to preferably adapt to the various shapes of objects. Then, the shape-adaptive set abstraction module is proposed to extract multiscale keypoints features by grouping the neighboring offset points' features, as well as features learned from adjacent raw points and the 2-D bird-view map. Finally, the region of interest (RoI)-grid proposal refinement module is used to aggregate the keypoints features for further proposal refinement and confidence prediction. Extensive experiments on the competitive KITTI 3D detection benchmark demonstrate that the proposed SASAN gains superior performance as compared with state-of-the-art methods.

Methodology and mechanisms of sustainable development of agricultural companies on the basis of competitive advantages

The problems of ensuring sustainable development of agricultural companies on the basis of their competitive advantages have been studied. The methodology of formation and development of mechanisms of sustainable development of the company on the basis of its competitive advantages is developed and the block diagram of the algorithm realising is drawn up. The developed methodology is based on the comparison of indicators of the managed and the leading company of the agro-industrial complex and the elimination of the difference on the basis of the development of sustainable development mechanisms and competitive advantages. The concept of sustainability of agricultural companies, which defines the state of continuous, dynamic development and ensures the current and future market demand for quality agricultural products, The mechanisms of sustainable development of the company are considered as an organisational subsystem of the campaign, implementing its activities to ensure sustainable development. The originality of the study lies in the development of an algorithm for the implementation of the proposed methodology for the formation of the mechanism of sustainable development of the company, providing sustainable development of the company on the basis of its competitive advantages and benchmarking.

Архітектура складових інституційного середовища розвитку циркулярної аграрної економіки

The article substantiates the composition, nature of action and role of the institutional environment in the context of accelerating the development of circular processes in socioeconomic agrarian systems and develops a conceptual scheme of the architecture of its components. The determinants of regional diffusion of the circular economy are defined. It is determined that there are three key determinants of endogenous and exogenous transformation towards the transition to circular business models: availability of knowledge, perception and understanding of benefits regarding the movement of material flows; availability, functioning and maturity of logistics and material value chains; values and models of business activity and cooperation. It is proved that the intensification of circular transformations in the economy requires a deep understanding of the mechanisms of movement of material and logistical flows, which underlie industrial symbiosis and ecosystems. Such systems reuse resources, reducing dependence on primary materials and optimizing costs. The shared use of products and resources is institutionalized through appropriate infrastructure and market mechanisms. In the conception proposed by the author, the structural architecture of the institutional environment for the development of circular agroeconomics consists of two main elements: formal and informal environment. The formal environment directly influences the motives, incentives and conditions of the business, facilitating the implementation of closed-loop business models. The informal environment acts indirectly, forming and supporting the values, perception and understanding by society of the importance of circular changes for present and future generations. This indirect influence is expressed in the formation of economic relations through consumer needs, preferences and requests, which creates demand in the market of goods and services and serves as a competitive benchmark for business.

Economic-financial performance and value creation in the Brazilian construction industry

PurposeThis paper aims to analyze the economic-financial performance (EFP) and value creation (VC) in the Brazilian construction industry.Design/methodology/approachBased on the theories of strategy and finance, a quantitative-qualitative, descriptive and explanatory and applied study was carried out, contrasting the performance of the Direcional company and the civil construction industry – both listed on the Brazilian Stock Exchange and the Over-the-Counter Market (B3)FindingsThe analysis of the EFP in the Brazilian construction industry shows that EZTEC, Helbor, Trisul and Direcional were the companies with the best EFP in the period. The analysis of the Economic Value Added (EVA®, henceforth EVA), as a VC metric and basis for assessing the relative technical efficiency score by Data Envelopment Analysis (DEA®, henceforth DEA), revealed that the companies Direcional, EZTEC, MRV and CR2 were considered efficient throughout the period covered. The multicriteria methodology for empirical testing of the EFP and VC allowed not only contrasts Direcional's results with the other companies of the construction industry but also offered a complementary tool for comparative analysis of enterprises of different sizes, structures and realities.Research limitations/implicationsRegardless of any contextual limitations, from a theoretical point of view, the research not only helps fill the research gap aforementioned but also expands knowledge on the topic and demonstrates how this multi-criteria methodology (integrating DEA and EVA) can be used to assess EFP and VC in addition to traditional tools. However, this new approach evaluates, at the same time, corporate and sectorial effectiveness by contrasting the efficiency and efficacy (simultaneously) in the generation of performance and value of a company in relation to the industry.Practical implicationsSignificant implications for managerial practice could be noted by offering a tool to improve company performance and creating a competitive benchmarking process for analysts, investors, managers, financing agencies, shareholders, policymakers and business owners, as well as organizations and sectors in similar situations – who need to assess the EFP and VC holistically and improve their decision-making processes.Originality/valueThe uniqueness and innovation of this research come from the original multi-criteria methodology developed, applied and validated for analysis of EFP and VC. This methodology was operationalized through DEA applied to the companies' EVA, making it possible to compare corporate results and those of the whole industry in a balanced way – an unexplored issue in the literature, especially in emerging economies, opening several avenues for future research.

Sparse generalized Yule–Walker estimation for large spatio-temporal autoregressions with an application to NO2 satellite data

We consider a high-dimensional model in which variables are observed over time and space. The model consists of a spatio-temporal regression containing a time lag and a spatial lag of the dependent variable. Unlike classical spatial autoregressive models, we do not rely on a predetermined spatial interaction matrix, but infer all spatial interactions from the data. Assuming sparsity, we estimate the spatial and temporal dependence fully data-driven by penalizing a set of Yule–Walker equations. This regularization can be left unstructured, but we also propose customized shrinkage procedures when observations originate from spatial grids (e.g. satellite images). Finite sample error bounds are derived and estimation consistency is established in an asymptotic framework wherein the sample size and the number of spatial units diverge jointly. Exogenous variables can be included as well. A simulation exercise shows strong finite sample performance compared to competing procedures. As an empirical application, we model satellite measured nitrogen dioxide (NO2) concentrations in London. Our approach delivers forecast improvements over a competitive benchmark and we discover evidence for strong spatial interactions.

Learning neighbor-enhanced region representations and question-guided visual representations for visual question answering

Great strides have been made in visual question answering field (VQA) based on the application and development of deep learning in related research fields. Existing models in this field focus on the learning and fusion of visual and textual features. However, it is extremely crucial for VQA tasks to focus on the associations between image regions and use question information to enhance key features. In this paper, we propose a method for mining and integrating neighbor-enhanced region representations and question-guided visual representations. Particularly, the region feature graph is first constructed to integrate the features of all regions and the relationships between regions. Secondly, a random walk-based method is presented to acquire the neighbor-enhanced region representations, which combines the topological relationships of all region nodes in the graph. The question-guided vertical and horizontal dual attention mechanism is then proposed to enhance the region representation from the region level and the feature level, respectively. Finally, the enhanced region representation and question representation are integrated adaptively to achieve answer prediction. Convincible experiments show that our method achieves improvements and outperforms prior state-of-the-art methods on two competitive benchmarks, i.e., VQA v1 and VQA v2.

Preprocessing of Propagation Redundant Clauses

The propagation redundant (PR) proof system generalizes the resolution and resolution asymmetric tautology proof systems used by conflict-driven clause learning (CDCL) solvers. PR allows short proofs of unsatisfiability for some problems that are difficult for CDCL solvers. Previous attempts to automate PR clause learning used hand-crafted heuristics that work well on some highly-structured problems. For example, the solver SaDiCaL incorporates PR clause learning into the CDCL loop, but it cannot compete with modern CDCL solvers due to its fragile heuristics. We present PReLearn, a preprocessing technique that learns short PR clauses. Adding these clauses to a formula reduces the search space that the solver must explore. By performing PR clause learning as a preprocessing stage, PR clauses can be found efficiently without sacrificing the robustness of modern CDCL solvers. On a large portion of SAT competition benchmarks we found that preprocessing with PReLearn improves solver performance. In addition, there were several satisfiable and unsatisfiable formulas that could only be solved after preprocessing with PReLearn. PReLearn supports proof logging, giving a high level of confidence in the results. Lastly, we tested the robustness of PReLearn by applying other forms of preprocessing as well as by randomly permuting variable names in the formula before running PReLearn, and we found PReLearn performed similarly with and without the changes to the formula.

Critical Classes and Samples Discovering for Partial Domain Adaptation.

Partial domain adaptation (PDA) attempts to learn transferable models from a large-scale labeled source domain to a small unlabeled target domain with fewer classes, which has attracted a recent surge of interest in transfer learning. Most conventional PDA approaches endeavor to design delicate source weighting schemes by leveraging target predictions to align cross-domain distributions in the shared class space. Accordingly, two crucial issues are overlooked in these methods. First, target prediction is a double-edged sword, and inaccurate predictions will result in negative transfer inevitably. Second, not all target samples have equal transferability during the adaptation; thus, "ambiguous" target data predicted with high uncertainty should be paid more attentions. In this article, we propose a critical classes and samples discovering network (CSDN) to identify the most relevant source classes and critical target samples, such that more precise cross-domain alignment in the shared label space could be enforced by co-training two diverse classifiers. Specifically, during the training process, CSDN introduces an adaptive source class weighting scheme to select the most relevant classes dynamically. Meanwhile, based on the designed target ambiguous score, CSDN emphasizes more on ambiguous target samples with larger inconsistent predictions to enable fine-grained alignment. Taking a step further, the weighting schemes in CSDN can be easily coupled with other PDA and DA methods to further boost their performance, thereby demonstrating its flexibility. Extensive experiments verify that CSDN attains excellent results compared to state of the arts on four highly competitive benchmark datasets.

Optimisation of carbon emission reduction in a competitive market with varying saturation and eco-conscious consumers

Non-saturated markets such as high-tech markets are attracting more and more eco-conscious consumers. Their utility can be increased by firms’ investment in carbon reduction, so the overall demand can be expanded without intensifying the market competition too much. In this paper, we build a two-stage-N-firm optimisation model by assuming firms’ products are horizontally differentiated and may engage in multi-dimensional competition. We analyze three scenarios: (1) Benchmark of price-only competition, (2) Two-dimensional ‘price + investment effort’ competition, and (3) N-firm circular differentiated competition. We find that consumers’ high eco-awareness may lead firms to lower investment levels for carbon emission reduction. When the market is sufficiently saturated, we reveal that a Prisoner’s Dilemma will occur where firms are trapped in investment-effort competition, so their profits are damaged. We further investigate the environmental performance and the social welfare, finding that consumers’ high eco-awareness may render the environment worse and undermine the social welfare.

Truck model recognition for an automatic overload detection system based on the improved MMAL-Net.

Efficient and reliable transportation of goods through trucks is crucial for road logistics. However, the overloading of trucks poses serious challenges to road infrastructure and traffic safety. Detecting and preventing truck overloading is of utmost importance for maintaining road conditions and ensuring the safety of both road users and goods transported. This paper introduces a novel method for detecting truck overloading. The method utilizes the improved MMAL-Net for truck model recognition. Vehicle identification involves using frontal and side truck images, while APPM is applied for local segmentation of the side image to recognize individual parts. The proposed method analyzes the captured images to precisely identify the models of trucks passing through automatic weighing stations on the highway. The improved MMAL-Net achieved an accuracy of 95.03% on the competitive benchmark dataset, Stanford Cars, demonstrating its superiority over other established methods. Furthermore, our method also demonstrated outstanding performance on a small-scale dataset. In our experimental evaluation, our method achieved a recognition accuracy of 85% when the training set consisted of 20 sets of photos, and it reached 100% as the training set gradually increased to 50 sets of samples. Through the integration of this recognition system with weight data obtained from weighing stations and license plates information, the method enables real-time assessment of truck overloading. The implementation of the proposed method is of vital importance for multiple aspects related to road traffic safety.

AI-Powered Virtual Screening of Large Compound Libraries Leads to the Discovery of Novel Inhibitors of Sirtuin-1.

The discovery of new scaffolds and chemotypes via high-throughput screening is tedious and resource intensive. Yet, there are millions of small molecules commercially available, rendering comprehensive in vitro tests intractable. We show how smart algorithms reduce large screening collections to target-specific sets of just a few hundred small molecules, allowing for a much faster and more cost-effective hit discovery process. We showcase the application of this virtual screening strategy by preselecting 434 compounds for Sirtuin-1 inhibition from a library of 2.6 million compounds, corresponding to 0.02% of the original library. Multistage in vitro validation ultimately confirmed nine chemically novel inhibitors. When compared to a competitive benchmark study for Sirtuin-1, our method shows a 12-fold higher hit rate. The results demonstrate how AI-driven preselection from large screening libraries allows for a massive reduction in the number of small molecules to be tested in vitro while still retaining a large number of hits.

Depthwise convolutional neural network for multiband automatic quasars classification in ATLAS

ABSTRACT In recent years, the astronomical scientific community has made significant efforts to automate quasars’ detection. Automatic classification of these objects is challenging since they are very distant and appear as point sources, outnumbered by other sources. Thus, performing automatic morphological classification is not straightforward; colour dimension seems better as a key concept. Previous work using machine learning tools has proposed classifiers that use features such as magnitude and colour, working only for quasar representation, which requires high-quality observational data that is not always available. Those features are computationally costly in extensive image surveys like VST ATLAS (Shanks et al. 2015). With the continuous developments in deep-learning architectures, we find a powerful tool to perform automatic classification from images, where capturing information from different bands takes relevance in this kind of approach. In this work, we developed a new quasar selection method that we hope to apply to the complete ATLAS survey in subsequent papers, where the completeness and efficiency of depthwise architecture will be compared to more standard methods such as selection on the colour–colour diagrams and machine-learning feature-based methods. This automatic quasar classification tool uses images in u, g, i, z bands available in ATLAS, heading towards new survey requirements facing the big data era. We propose a deep-learning architecture based on depthwise convolutional units that work directly with ATLAS images, reduced by the VST pipeline. Our model reaches an accuracy of 96.53 per cent with a quasar classification f1-score of 96.49 per cent, a very competitive benchmark compared to previous unscalable approaches.

A two-channel deep network based model for improving ultra-short-term prediction of wind power via utilizing multi-source data

Data-driven predictions of wind turbine power outputs have received numerous discussions with using turbine data from the supervisory control and data acquisition (SCADA) system itself. It is of a high curiosity on studying the feasibility of extending the prediction capability with additionally considering data of other sources and the data-driven modeling principle of doing so. To respond such problem and introduce more innovative prediction techniques, this paper presents a pioneering attempt of studying a two-channel deep network modeling method for wind power predictions which leverage both the wind farm data and farm geoinformation. Novelty can be profiled as follows: 1) To accommodate multi-source data in the input, a new high-dimensional input form of two components, a tensor and a graph, is developed; 2) To advance predictions with such input, a deep graph attention convolutional recurrent (GACR) method, which develops one novel deep network channel with stacking multiple graph convolution and long short term memory (GCN-LSTM) layers for engineering high-level latent features from high-dimensional inputs and another classical feature selection channel for directly engaging valuable wind turbine attributes, is proposed. Comprehensive computational experiments are conducted to verify the value of such modeling development by comparing it with a set of competitive benchmarking models. An ablation study is also conducted to explain the necessity and value of network modules in the proposed method. A new state-of-the-art prediction performance is achieved.

Multi-scale network via progressive multi-granularity attention for fine-grained visual classification

Fine-grained visual classification (FGVC) is challenging due to the subtle inter-class variations. Key region location and discriminative feature extraction are the crucial aspects of FGVC. In this paper, we carry out research from the perspective of multi-granularity and multi-scale, explore the information in different granularities and scales, and propose a novel method called Multi-Scale Network via Progressive Multi-Granularity Attention (MSMGA-Net), which locates the key discriminative regions while avoiding recognition confusion caused by subtle inter-class variations. In order to achieve discriminative region locating, we adopt a progressive training strategy to make the network work in steps during training process, and the jigsaw puzzle generator (JPGen) is constructed to generate images with different granularity for different training steps. The focus of each step of training is to locate the key discriminative regions of specific granularity image at the corresponding stage of the network through the multi-granularity attention (MGA) module. Aiming to obtain the discriminative features, we also construct a multi-scale convolution (MSC) module for feature extraction corresponding to each step respectively, enabling the capture of different scales of details. We validated our MSMGA-Net through extensive experiments on CUB-200-2011, FGVC Aircraft and Stanford Cars. The experiments demonstrate that MSMGA-Net can get promising results. In particular, the accuracy on two publicly competitive benchmark datasets, CUB-200-2011 and FGVC Aircraft, reached 90.30% and 93.84%, respectively.

Fine-grained Image Recognition via Attention Interaction and Counterfactual Attention Network

Learning subtle and discriminative regions plays an important role in fine-grained image recognition, and attention mechanisms have shown great potential in such tasks. Recent research mainly focuses on employing the attention mechanism to locate key discriminative regions and learn salient features, whilst ignoring imperceptible complementary features and the causal relationship between prediction results and attention. To address the above issues, we propose an Attention Interaction and Counterfactual Attention Network (AICA-Net). Specifically, we propose an Attention Interaction Fusion Module (AIFM) to model the negative correlation between the attention map channels to locate the complementary features, and fuse the complementary features and key discriminative features to generate richer fine-grained features. Simultaneously, an Enhanced Counterfactual Attention Module (ECAM) is proposed to generate a counterfactual attention map. By comparing the impact of the learned attention map and the counterfactual attention map on the final prediction results, quantifying the quality of attention drives the network to learn more effective attention. Extensive experiments on CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets have shown that our AICA-Net can get outstanding results. In particular, it achieves 90.83% and 95.87% accuracy on two open competitive benchmark datasets CUB-200-2011 and Stanford Cars, respectively. Experiments demonstrate that our method outperforms state-of-the-art solutions.

N-list-enhanced heuristic for distributed three-stage assembly permutation flow shop scheduling

AbstractSystem-wide optimization of distributed manufacturing operations enables process improvement beyond the standalone and individual optimality norms. This study addresses the production planning of a distributed manufacturing system consisting of three stages: production of parts (subcomponents), assembly of components in Original Equipment Manufacturer (OEM) factories, and final assembly of products at the product manufacturer’s factory. Distributed Three Stage Assembly Permutation Flowshop Scheduling Problems (DTrSAPFSP) models this operational situation; it is the most recent development in the literature of distributed scheduling problems, which has seen very limited development for possible industrial applications. This research introduces a highly efficient constructive heuristic to contribute to the literature on DTrSAPFSP. Numerical experiments considering a comprehensive set of operational parameters are undertaken to evaluate the performance of the benchmark algorithms. It is shown that the N-list-enhanced Constructive Heuristic algorithm performs significantly better than the current best-performing algorithm and three new metaheuristics in terms of both solution quality and computational time. It can, therefore, be considered a competitive benchmark for future studies on distributed production scheduling and computing.