Reviewerly/exHarmony huggingface dataset:
A Lightweight Feature Distillation and Enhancement Network for Super-Resolution Remote Sensing Images

Super-resolution (SR) images based on deep networks have achieved great accomplishments in recent years, but the large number of parameters that come with them are not conducive to use in equipment with limited capabilities in real life. Therefore, we propose a lightweight feature distillation and enhancement network (FDENet). Specifically, we propose a feature distillation and enhancement block (FDEB), which contains two parts: a feature-distillation part and a feature-enhancement part. Firstly, the feature-distillation part uses the stepwise distillation operation to extract the layered feature, and here we use the proposed stepwise fusion mechanism (SFM) to fuse the retained features after stepwise distillation to promote information flow and use the shallow pixel attention block (SRAB) to extract information. Secondly, we use the feature-enhancement part to enhance the extracted features. The feature-enhancement part is composed of well-designed bilateral bands. The upper sideband is used to enhance the features, and the lower sideband is used to extract the complex background information of remote sensing images. Finally, we fuse the features of the upper and lower sidebands to enhance the expression ability of the features. A large number of experiments show that the proposed FDENet both produces less parameters and performs better than most existing advanced models.
High-Speed Variable Polynomial Toeplitz Hash Algorithm Based on FPGA

In the Quantum Key Distribution (QKD) network, authentication protocols play a critical role in safeguarding data interactions among users. To keep pace with the rapid advancement of QKD technology, authentication protocols must be capable of processing data at faster speeds. The Secure Hash Algorithm (SHA), which functions as a cryptographic hash function, is a key technology in digital authentication. Irreducible polynomials can serve as characteristic functions of the Linear Feedback Shift Register (LFSR) to rapidly generate pseudo-random sequences, which in turn form the foundation of the hash algorithm. Currently, the most prevalent approach to hardware implementation involves performing block computations and pipeline data processing of the Toeplitz matrix in the Field-Programmable Gate Array (FPGA) to reach a maximum computing rate of 1 Gbps. However, this approach employs a fixed irreducible polynomial as the characteristic polynomial of the LFSR, which results in computational inefficiency as the highest bit of the polynomial restricts the width of parallel processing. Moreover, an attacker could deduce the irreducible polynomials utilized by an algorithm based on the output results, creating a serious concealed security risk. This paper proposes a method to use FPGA to implement variational irreducible polynomials based on a hashing algorithm. Our method achieves an operational rate of 6.8 Gbps by computing equivalent polynomials and updating the Toeplitz matrix with pipeline operations in real-time, which accelerates the authentication protocol while also significantly enhancing its security. Moreover, the optimization of this algorithm can be extended to quantum randomness extraction, leading to a considerable increase in the generation rate of random numbers.
An Advanced Job Scheduling Algorithmic Architecture to Reduce Energy Consumption and CO2 Emissions in Multi-Cloud

Cloud Computing is one of the emerging fields in the modern-day world. Due to the increased volume of job requests, job schedulers have received updates one at a time. The evolution of machine learning in the context of cloud schedules has had a significant impact on cost reduction in terms of energy consumption and makespan. The research article presents a two-phase process for the scheduling architecture of cloud computing where PMs are the main working unit and users are supplied to the PMs based on the work abilities of the PM in terms of resources. A minimum cost is desired in the preliminary phase of the allocation of the user to the PM. A clustered approach utilizing k-means and Q-learning was imposed to migrate the users from one PM to another PM based on Quality of Service (QoS) parameters. The proposed work has also incorporated CO2 emissions as a major evaluation parameter other than energy consumption. To support resource sharing, the deployment model is a multi-cloud model. The proposed work is evaluated against other recently proposed state of the art techniques on the basis of QoS parameters and the proposed work proved to be better in terms of efficiency at the end of the draft.
Assessment of 2D Digital Image Correlation for Experimental Modal Analysis of Transient Response of Beams Using a Continuous Wavelet Transform Method

The modal parameters of structures, and in particular their mode shapes, are generally determined based on the measurement of accelerometers or laser vibrometers. However, these sensors do not allow the performance of full-field measurements. In this study, the free vibration of a beam triggered by a shock is investigated using a high-speed camera with high image definition. With the help of digital image correlation (DIC), the beam displacement fields are deduced from the images. To analyse the DIC measurement quality, different tests and analyses are performed. First, the systematic errors and uncertainties in the DIC calculation for a simple translation are analysed considering different speckles. Then, tests on two configurations of a vibrating beam are filmed and full-field displacement measurements are computed. The modal parameters of the beam are deduced from these measurements using a continuous wavelet transform method. Particular care was taken to adapt the method to the post-processing of the numerous and noisy signals obtained for these experiments. All the steps of the post-processing are detailed in this paper. Finally, the modal parameters obtained with the proposed method are compared with those obtained in a more classical way using accelerometers and from the beam theory. In particular, the comparison of the signal-to-noise ratio of the different measurement methods is discussed.
Who did what and when? The effect of chatbots’ service recovery on customer satisfaction and revisit intention

Purpose This study aims to explore the recovery performances of chatbots (vs human employees) and help firms use chatbots to carry out effective service recovery. Design/methodology/approach Two experiments were conducted to test the proposed hypotheses. Findings The results show that compared with human employees’ recovery, chatbots’ recovery leads to lower customer satisfaction and revisit intention. This effect is more significant for symbolic recovery instead of economic recovery. Perceived distributive and interactional justice mediate the interaction effect of recovery provider and recovery strategy on recovery performance. Using immediate recovery rather than delayed recovery can attenuate chatbots’ poor performances in symbolic recovery. Originality/value This study enriches the chatbot research and the service recovery literature by deploying chatbots into the service recovery setting. Using an integrated theoretical model including recovery strategy and recovery timing, this study provides substantive insight into how firms can enhance chatbots’ recovery performances.
Short-term prediction of PV output based on weather classification and SSA-ELM

In this paper, according to the power output characteristics of distributed photovoltaic users, the SSA-ELM (Sparrow Search Algorithm - Extreme Learning Machine) model based on weather type division is proposed for photovoltaic power day ahead prediction. Because the solar panel power generation sequence of photovoltaic users contains high frequency fluctuations, in this paper we use the power sequence convergence effect to make cluster prediction on all photovoltaic panels to reduce the randomness of distributed photovoltaic. The prediction accuracy is further improved by dividing weather types. The historical data of distributed PV users in a region of Gansu province is used for modeling verification, and the results show that the prediction error of the proposed method is lower. In bad weather, the root mean square error is at least 0.02 less than the comparison model, and the average annual accuracy rate is 93.2%, which proves the applicability of the proposed method in different output types.
Federated learning based privacy preserved English accent training ecosystem for people with Indian language accent

Language refers to the system of communication used by a community, while accent refers to the distinct way in which an individual or group pronounces words or phrases within that language. This paper aims to design and develop a privacy-preserving federated learning-based intervention for individuals to identify a recommending system for the improvement of English accents. In methodology, the deep learning algorithm is trained and validated using the AccentDB dataset and further same model is implemented over Federated Learning Ecosystem. Data in terms of identical and independent distribution is analyzed to ensure the productivity of the trained model in data constraint environments. The results section is focused to analyse the utilization of proposed implementations. Further, the trained model is applied to mobile and web applications for accessing levels and then training English accents to individuals. Similarly, to analyze incremental learning through federated learning, the implementation is analyzed on basis of parameters viz., accuracy, precision, recall, and loss. In the future, this proposed system could be embedded with various other applications for the overall enhancement of effective communication skills.
ConsenTrack—Blockchain Based Framework for Open Banking Consent Data Tracking

Abstract Consent management is most critical part of open banking. Customers, banks, third party service providers, regulators are various parties involved into this process. The recent data shows that open banking has not been greatly accepted yet by customers to the fullest capability. Recent surveys conducted on usage of open banking indicates the discomfort in customer mind about data sharing. Blockchain based framework implementation can bring the required transparency into the consent management process. To achieve that Blockchain technology needs to be embraced by banks and Third party providers (TPPs) to provide customers the open banking services in transparent manner. A blockchain based framework which can be easily integrated into banks’ existing technology landscape thus becomes need of the hour. Consortium permissioned blockchain based framework implemented in Corda is suggested in this paper which addresses challenges faced by customers and it tracks data sharing violation for communicating to customers. Data sharing between bank and TPPs happen as node to node transaction and regulatory bodies can have tracking of every such transactions as owner of Notary node. Based on the legal contract between bank and TPP, framework compares and finds out in real time if any data sharing violation happening. Real-time tracking of data sharing violation and communication to customer provides transparency into the framework which will boost customer confidence and trust into the system. Regulatory bodies need to actively own this part to share information with customers about the data handling if there is any violation .
Real Time Web-Based Smart Attendance System using AI

The performance of automatic face recognition (AFR) technology has improved in recent years. As a result, these systems are frequently employed for security and business purposes. a face recognition programme that runs automatically in the background while a university tracks employee attendance. Hence, Smart Attendance with Real Time Face Recognition might be a global solution for managing personnel on a daily basis. The work is incredibly challenging since the critical temporal background subtraction during an image remains a challenge. Real-time face detection is done using easy, quick Principal Component Analysis, which has a high rate of success in identifying the faces found. As a result, we have developed a module in which an employee's attendance will be recorded automatically by recognising their face using face recognition technology. Their face will be saved in the database after this one-time enrollment process. Since enrolling a face is a one-time activity, we need a mechanism. Every employee will have a unique employee ID that is their own roll number. Every employee's presence will be updated within a database. Results of the proposed system demonstrated that it performs better than the manual attendance system. The marking of attendance follows employee identification. This application provides the customer with considerably more solutions and accurate leads than the conventional attendance and leave.
A multi‐stage model for bird's eye view prediction based on stereo‐matching model and RGB‐D semantic segmentation

Abstract Bird's‐Eye‐View (BEV) map is a powerful and detailed scene representation for intelligent vehicles that provides both the location and semantic information about nearby objects from a top‐down perspective. BEV map generation is a complex multi‐stage task, and the existing methods typically perform poorly for distant scenes. Thus, the authors introduce a novel multi‐stage model that infers to obtain more accurate BEV map. First, the authors propose the Adaptive Aggregation with Stereo Mixture Density (AA‐SMD) model, which is an improved stereo matching model that eliminates bleeding artefacts and provides more accurate depth estimation. Next, the authors employ the RGB‐Depth (RGB‐D) semantic segmentation model to improve the semantic segmentation performance and connectivity of their model. The depth map and semantic segmentation maps are then combined to create an incomplete BEV map. Finally, the authors propose a Multi Strip Pooling Unet (MSP‐Unet) model with a hierarchical multi‐scale (HMS) attention and strip pooling (SP) module to improve prediction with BEV generation. The authors evaluate their model with a Car Learn to Act (CARLA)‐generated synthetic dataset. The experiment results demonstrate that the authors’ model generates a highly accurate representation of the surrounding environment achieving a state‐of‐the‐art result of 61.50% Mean Intersection‐over‐Union (MIoU) across eight classes.
Comparing Mechanical Neural-Network Learning Algorithms

Abstract The purpose of this work is to compare learning algorithms to identify which is the fastest and most accurate for training mechanical neural networks (MNNs). MNNs are a unique class of lattice-based artificial intelligence (AI) architected materials that learn their mechanical behaviors with repeated exposure to external loads. They can learn multiple behaviors simultaneously in situ and re-learn desired behaviors after being damaged or cut into new shapes. MNNs learn by tuning the stiffnesses of their constituent beams similar to how artificial neural networks (ANNs) learn by tuning their weights. In this work, we compare the performance of six algorithms (i.e., genetic algorithm, full pattern search, partial pattern search, interior point, sequential quadratic progression, and Nelder–Mead) applied to MNN leaning. A computational model was created to simulate MNN learning using these algorithms with experimentally measured noise included. A total of 3900 runs were simulated. The results were validated using experimentally collected data from a physical MNN. We identify algorithms like Nelder–Mead that are both fast and able to reject noise. Additionally, we provide insights into selecting learning algorithms based on the desired balance between accuracy and speed, as well as the general characteristics that are favorable for training MNNs. These insights will promote more efficient MNN learning and will provide a foundation for future algorithm development.
An Explainable Artificial Intelligence Approach for Multi-Criteria ABC Item Classification

Multi-criteria ABC classification is a useful model for automatic inventory management and optimization. This model enables a rapid classification of inventory items into three groups, having varying managerial levels. Several methods, based on different criteria and principles, were proposed to build the ABC classes. However, existing ABC classification methods operate as black-box AI processes that only provide assignments of the items to the different ABC classes without providing further managerial explanations. The multi-criteria nature of the inventory classification problem makes the utilization and the interpretation of item classes difficult, without further information. Decision makers usually need additional information regarding important characteristics that were crucial in determining the managerial classes of the items because such information can help managers better understand the inventory groups and make inventory management decisions more transparent. To address this issue, we propose a two-phased explainable approach based on eXplainable Artificial Intelligence (XAI) capabilities. The proposed approach provides both local and global explanations of the built ABC classes at the item and class levels, respectively. Application of the proposed approach in inventory classification of a firm, specialized in retail sales, demonstrated its effectiveness in generating accurate and interpretable ABC classes. Assignments of the items to the different ABC classes were well-explained based on the item’s criteria. The results in this particular application have shown a significant impact of the sales, profit, and customer priority as criteria that had an impact on determining the item classes.
A chemometrics-based approach for the chemical prediction of lead (Pb) levels in surface soil, Dammam, Saudi Arabia

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Video analytic system for activity profiling, fall detection, and unstable motion detection

Abstract Real time detection of falls and unstable movement by elderly people is vital to their quality of life and safety. We present an edge processing device integrated with a cloud computation framework that can be used for activity profiling as well as trigger alerts for falls and unstable motion by elderly people at home. The proposed system uses fixed cameras to track and analyze each visible person in the scene, classifying their actions into nine ordinary activities, a fall, or unstable movement. An alert notification is sent to caregivers whenever a fall or unstable movement is detected. The major components of the system include an embedded device (NVIDIA JETSON TX2) and cloud-based storage and analysis infrastructure. The system is composed of modules for detecting, tracking and recognizing humans, a cascaded hierarchical classifier for nine ordinary activities and falls, and a long short-term memory (LSTM) module to predict unstable movement in video. The system is designed for accuracy, usability, and cost. A prototype system has been subjected to individual module tests along with a field test within a volunteer’s household. It achieved an accuracy of 91.6% for ordinary actions and falls with a recall of 97.02% for unstable motion. Future phases will expand deployment to multiple homes.
BackTracer: Improving ray-casting 3D target acquisition by backtracking the interaction history

Ray-casting is a fundamental 3D target acquisition technique in virtual environments. With ray-casting, users emit a ray to highlight a desired target before pressing a button to select it; however, due to hand tremor, tracker jitter, and the Heisenberg effect, the ray direction can be shifted during the button pressing process, causing wrong selections. To improve selection accuracy, instead of selecting an object at a button press, we backtrack the interaction history to the time when the desired target is highlighted and select that instead. Inspired by Kopper et al.’s predictive model of distal pointing, we hypothesized that the optimal backtrack time depends on the object’s angular size. We validated our hypothesis through an experiment and implemented BackTracer based on the results. Moreover, BackTracer was evaluated against basic ray-casting, state-of-the-art history-based ray-casting, and state-of-the-art progressive refinement ray-casting, and results indicated that BackTracer significantly improved the accuracy and selection efficiency, making it a promising technique for 3D target acquisition.
Ore composition and native gold characteristics of an ore occurrence in Eastern Transbaikalia

The article deals with the characteristics of the ores of an ore occurrence of the Munginsky cluster located on the western flank of one of the commercial fields of Eastern Transbaikalia. The ores of this promising ore occurrence are underexplored, especially in the technological aspect, which served the basis for studying their composition and characterizing native gold as their main commercial component. The composition of ores and altered rocks was studied by the following methods: atomic emission spectral quantitative analysis, X-ray fluorescence, as well as phase atomic absorption and atomic emission analysis with inductively coupled plasma ICP-AES. The mass fraction of total and organic carbon was determined in the Central Fire Assay Laboratory of JSC Pokrovsky Rudnik, Blagoveshchensk. The proportion of carbonate carbon dioxide was determined by the method of titrimetric determination of carbon dioxide. The content of gold was given according to the data of assay melting, the content of silver – according to the data of atomic absorption analysis. Petrographic and mineragraphic studies were carried out using an optical microscope Olympus BX-51. The mineral composition of the ore sample was also estimated based on the results of X-ray diffraction analysis. The quantitative mineral composition was studied on the crushed material of the original ore with a particle size of -2 mm using the microscopic study data of transparent and polished thin sections on a microscope Nikon Eclipse LV 100 POL. The content of iron hydroxides was determined from the weight difference after the sample was treated with a 10 % oxalic acid solution in a water bath. Comparison of the results obtained on the ore occurrence with the materials on the ore composition of the closest deposit located in 30 km demonstrates that on the one hand they are similar and uniform, and on the other hand they feature some differences. Their common parameters include a rather high degree of sulfide content of ores and similar chemical and mineral composition with the predominance of arsenopyrite, an increased role of bismuth as well as the predominance of fine gold and its similar morphological characteristics. This fact allows to consider the ore occurrence under investigation as an integral part of a single ore system of the closest deposit.