Reviewerly/exHarmony huggingface dataset:
DEIM: An effective deep encoding and interaction model for sentence matching

Sentence matching is to compare the relevance of two paragraphs of sentences. The current mainstream approach is a deep learning-based approach, usually through the attention mechanism to interact with sentence pairs, which can be a good match for the relationship between two sentences. However, these methods cannot handle sentences with high semantic complexity. Therefore, we propose a method that is based on a deep encoder to extract deep semantic information. To avoid the problem of an insufficient number of interactions to lack information, we encode sentences at the coding layer using the method of multiple encoded interactions. After we get the representation information of the sentence, in this paper, the encoded sentences are processed for secondary interaction of information, while using self-attention to obtain more complex feature interaction information. We conducted experimental validation on some text-matching datasets, and the experimental results show that our proposed DEIM model plays an irreplaceable and important role in the task of text matching.
Improved Northern Goshawk Optimization Algorithm for Global Optimization

Global optimization has been used in many real-world problems. Nature-inspired meta-heuristic algorithms, such as the Northern Goshawk Optimization NGO algorithm that has just been proposed, are often used to solve these kinds of optimization problems. An NGO provides satisfactory results. In this algorithm, the proposed exploration model may not provide sufficient coverage of the problem space, trapping the system in a local optimal solution. To improve the performance of NGO, a novel and efficient improved northern goshawk optimization technique named INGO is proposed in this paper. In INGO, a new concept of switching between exploration and exploitation has been developed to improve overall algorithm performance to avoid being stuck in local optima. Also, to increase search capabilities, Levy Flight is used. Twenty-three known benchmark functions were used to test the performance of the proposed INGO. The results were compared to those of an NGO and some well-known robust algorithms. Experimental data indicates that the INGO suggested in this study consistently outperforms the traditional NGO and alternative methods in a significant number of test functions.
Horizontal Pod Autoscaling based on Kubernetes with Fast Response and Slow Shrinkage

Autoscaling is an important part of Kubernetes. Horizontal Pod Autoscaling (HPA) schedules cluster resources according to the service load status to ensure that services are still running normally when the load increases or decreases. But the default HPA is slow and inflexible. In scenarios with large load changes, it will scale at a high frequency, resulting in a waste of cluster resources and reducing the robustness of the cluster. This paper proposes a Fast-response and Slow-shrinkage algorithm. By setting the expansion rate, tolerance, window time, etc., the flexibility of scaling is increased. When facing the pressure of a sudden increase in traffic, the rapid expansion can be optimized in minutes. The algorithm also adopts a cautious shrinking strategy to reduce and prevent failures caused by secondary access peaks.
Effects of virtual reality’s viewing medium and the environment’s spatial openness on divergent thinking

Virtual Reality (VR) technology is used in various fields, and research on VR creative activities has been widely conducted. This study examined the effects of VR environment on divergent thinking, a component of creative thinking. Specifically, two experiments were conducted to test the prediction that viewing visually open VR environments with immersive head-mounted displays (HMD) affects divergent thinking. Divergent thinking was evaluated using Alternative Uses Test (AUT) scores; AUT was performed while the participants viewed the experiment stimuli. In Experiment 1, I manipulated the VR viewing medium by having one group view a 360° video with an HMD and a second group view the same video on a computer screen. Additionally, I established a control group that viewed a real-world laboratory instead of the videos. The HMD group showed higher AUT scores than the computer screen group. In Experiment 2, I manipulated the spatial openness of a VR environment by having one group view a 360° video of a visually open coast and a second group view a 360° video of a visually closed laboratory. The coast group showed higher AUT scores than the laboratory group. In conclusion, exposure to a visually open VR environment on an HMD promotes divergent thinking. The limitations of this study and suggestions for further research are discussed.
An efficient biobjective evolutionary algorithm for mining frequent and high utility itemsets

Mining frequent and high utility itemsets (FHUIs) from transactional databases is essential in data mining. From a multiobjective perspective, modelling the task of mining FHUIs in a unified framework requires support and utility to be considered simultaneously. In contrast to traditional algorithms for mining FHUIs, multiobjective evolutionary algorithms (MOEAs) can overcome the difficulty of setting the parameter and can generate multiple solutions in one pass, which brings advantages to mining FHUIs. However, MOEAs may be inefficient when the number of transactions and the number of items in the transaction database are large. To address this problem, we propose an efficient biobjective evolutionary algorithm for obtaining FHUIs (BOEA-FHUI) based on three novel strategies. In BOEA-FHUI, a pruning strategy is proposed to reduce the search space. Based on the pruning results, a repair strategy is proposed to make the generated inferior offspring jump out of the dominated region of the previous Pareto solutions. With the proposed pruning and repair strategies, the search space can be significantly reduced, which helps improve the search efficiency. To increase the number of items with higher support and higher utility values, an improved mutation strategy based on the sparse nature of the FHUI is proposed, which can accelerate the convergence speed of the algorithm. The experimental results on the real-world and synthetic datasets show that the proposed algorithm performs better than state-of-the-art MOEAs in finding FHUIs.
Task parallelism-based architectures on FPGA to optimize the energy efficiency of AI at the edge

In the world of artificial intelligence (AI) at the edge, we need to focus primarily on the energy efficiency with which we approach deep neural network (DNN) applications. In many applications, the speed of obtaining an inference can be critical; but many applications easily meet their time requirements, and the energy needed to calculate the huge numbers of multiplication and addition operations of DNNs becomes the essential element. We have provided systolic architectural solutions written in C++ and OpenCL that are highly flexible and easily tunable to take full advantage of the resources of programmable devices and achieve superior energy efficiencies. We focused on low-cost solutions with soft macro microprocessors (Nios2) and hard macro microprocessors (ARM cortex A9).
Concept Drift in Software Defect Prediction: A Method for Detecting and Handling the Drift

Software Defect Prediction (SDP) is crucial towards software quality assurance in software engineering. SDP analyzes the software metrics data for timely prediction of defect prone software modules. Prediction process is automated by constructing defect prediction classification models using machine learning techniques. These models are trained using metrics data from historical projects of similar types. Based on the learned experience, models are used to predict defect prone modules in currently tested software. These models perform well if the concept is stationary in a dynamic software development environment. But their performance degrades unexpectedly in the presence of change in concept (Concept Drift). Therefore, concept drift (CD) detection is an important activity for improving the overall accuracy of the prediction model. Previous studies on SDP have shown that CD may occur in software defect data and the used defect prediction model may require to be updated to deal with CD. This phenomenon of handling the CD is known as CD adaptation. It is observed that still efforts need to be done in this direction in the SDP domain. In this article, we have proposed a pair of paired learners (PoPL) approach for handling CD in SDP. We combined the drift detection capabilities of two independent paired learners and used the paired learner (PL) with the best performance in recent time for next prediction. We experimented on various publicly available software defect datasets garnered from public data repositories. Experimentation results showed that our proposed approach performed better than the existing similar works and the base PL model based on various performance measures.
Grammar-Supervised End-to-End Speech Recognition with Part-of-Speech Tagging and Dependency Parsing

For most automatic speech recognition systems, many unacceptable hypothesis errors still make the recognition results absurd and difficult to understand. In this paper, we introduce the grammar information to improve the performance of the grammatical deviation distance and increase the readability of the hypothesis. The reinforcement of word embedding with grammar embedding is presented to intensify the grammar expression. An auxiliary text-to-grammar task is provided to improve the performance of the recognition results with the downstream task evaluation. Furthermore, the multiple evaluation methodology of grammar is used to explore an expandable usage paradigm with grammar knowledge. Experiments on the small open-source Mandarin speech corpus AISHELL-1 and large private-source Mandarin speech corpus TRANS-M tasks show that our method can perform very well with no additional data. Our method achieves relative character error rate reductions of 3.2% and 5.0%, a relative grammatical deviation distance reduction of 4.7% and 5.9% on AISHELL-1 and TRANS-M tasks, respectively. Moreover, the grammar-based mean opinion score of our method is about 4.29 and 3.20, significantly superior to the baseline of 4.11 and 3.02.
VICO-DR: A Collaborative Virtual Dressing Room for Image Consulting

In recent years, extended reality has increasingly been used to enhance the shopping experience for customers. In particular, some virtual dressing room applications have begun to develop, as they allow customers to try on digital clothes and see how they fit. However, recent studies found that the presence of an AI or a real shopping assistant could improve the virtual dressing room experience. In response to this, we have developed a collaborative synchronous virtual dressing room for image consulting that allows customers to try on realistic digital garments chosen by a remotely connected human image consultant. The application has different features for the image consultant and the customer. The image consultant can connect to the application, define a database of garments, select different outfits with different sizes for the customer to try, and communicate with the customer through a single RGB camera system. The customer-side application can visualize the description of the outfit that the avatar is wearing, as well as the virtual shopping cart. The main purpose of the application is to offer an immersive experience, ensured by the presence of a realistic environment, an avatar that resembles the customer, a real-time physically-based cloth simulation algorithm, and a video-chat system.
CyL-GHI: Global Horizontal Irradiance Dataset Containing 18 Years of Refined Data at 30-Min Granularity from 37 Stations Located in Castile and León (Spain)

Accurate solar forecasting lately relies on advances in the field of artificial intelligence and on the availability of databases with large amounts of information on meteorological variables. In this paper, we present the methodology applied to introduce a large-scale, public, and solar irradiance dataset, CyL-GHI, containing refined data from 37 stations found within the Spanish region of Castile and León (Spanish: Castilla y León, or CyL). In addition to the data cleaning steps, the procedure also features steps that enable the addition of meteorological and geographical variables that complement the value of the initial data. The proposed dataset, resulting from applying the processing methodology, is delivered both in raw format and with the quality processing applied, and continuously covers 18 years (the period from 1 January 2002 to 31 December 2019), with a temporal resolution of 30 min. CyL-GHI can result in great importance in studies focused on the spatial-temporal characteristics of solar irradiance data, due to the geographical information considered that enables a regional analysis of the phenomena (the 37 stations cover a land area larger than 94,226 km2). Afterwards, three popular artificial intelligence algorithms were optimised and tested on CyL-GHI, their performance values being offered as baselines to compare other forecasting implementations. Furthermore, the ERA5 values corresponding to the studied area were analysed and compared with performance values delivered by the trained models. The inclusion of previous observations of neighbours as input to an optimised Random Forest model (applying a spatio-temporal approach) improved the predictive capability of the machine learning models by almost 3%.
SiamFFN: Siamese Feature Fusion Network for Visual Tracking

Siamese network-based trackers have developed rapidly in the field of visual object tracking recently. Many Siamese network-based trackers currently in use rely on result fusion to combine the classification result map and regression result map. However, these result maps are obtained from the multi-level feature map and are independent of each other. It is inappropriate and flawed to use result fusion. Additionally, classification module and regression module are independent of each other, which leads to feature misalignment. In this paper, we propose a feature-fusion approach that involves fusing similarity response maps using a novel scale attention mechanism and subsequently decoding the features. To reduce the feature misalignment and produce more precise tracking results, we suggest using Classification Supervised Regression Loss (CSRL), to train the model. Experiments conducted on three challenging benchmark datasets show that this method outperforms current models in terms of both performance and efficiency, running at 40 fps.
Plug‐and‐Play Continuous Variable Measurement‐Device‐Independent Quantum Key Distribution

Abstract In this paper, a continuous variable (CV) measurement‐device‐independent (MDI) quantum key distribution (QKD) protocol using Gaussian modulated coherent states is proposed. The MDI is first proposed to resist the attacks on the detection equipment by introducing an untrusted relay. However, the necessity of propagation of local oscillator between legitimate users and the relay makes the implementation of CV‐MDI‐QKD highly impractical. By introducing the plug‐and‐play (P&amp;P) technique into CV‐MDI‐QKD, the problems of polarization drifts caused by environmental disturbance and the security loopholes during the local oscillator transmission are solved naturally. The proposed scheme is superior to the previous CV‐MDI‐QKD protocol on the aspect of implementation. The security bounds of the P&amp;P CV‐MDI‐QKD under the Gaussian collective attack are analyzed. It is believed that the technique presented in this paper can be extended to quantum network.
A High-Performance FPGA-Based Depthwise Separable Convolution Accelerator

Depthwise separable convolution (DSC) significantly reduces parameter and floating operations with an acceptable loss of accuracy and has been widely used in various lightweight convolutional neural network (CNN) models. In practical applications, however, DSC accelerators based on graphics processing units (GPUs) cannot fully exploit the performance of DSC and are unsuitable for mobile application scenarios. Moreover, low resource utilization due to idle engines is a common problem in DSC accelerator design. In this paper, a high-performance DSC hardware accelerator based on field-programmable gate arrays (FPGAs) is proposed. A highly reusable and scalable multiplication and accumulation engine is proposed to improve the utilization of computational resources. An efficient convolution algorithm is proposed for depthwise convolution (DWC) and pointwise convolution (PWC), respectively, to reduce the on-chip memory occupancy. Meanwhile, the proposed convolution algorithms achieve partial fusion between PWC and DWC, and improve the off-chip memory access efficiency. To maximise bandwidth utilization and reduce latency when reading feature maps, an address mapping method for off-chip accesses is proposed. The performance of the proposed accelerator is demonstrated by implementing MobileNetV2 on an Intel Arria 10 GX660 FPGA by using Verilog HDL. The experimental results show that the proposed DSC accelerator achieves a performance of 205.1 FPS, 128.8 GFLOPS, and 0.24 GOPS/DSP for input images of size 224×224×3.
“Harnessing Customer Feedback for Product Recommendations: An Aspect-Level Sentiment Analysis Framework”

Abstract This research paper presents a novel approach for recommending products to customers based on their cared aspects by performing sentiment analysis on customer feedback. The proposed approach utilizes the WordNet database to identify and extract aspects from customer reviews and feedback, and then applies sentiment analysis techniques to determine the sentiment associated with each aspect. The resulting sentiment scores are then used to generate personalized product recommendations that align with the customer’s preferences and priorities. Here we extract the comments from an e-commerce website that is Amazon, and we then choose the most cared aspects from those comments. The dataset is publicly available online which contains reviews of each product. The chosen most cared aspects are price, colour, battery, and screen. These cared aspects are keywords that shopping online and recommending, will help to categorize the comments based on price, colour, battery, and screen. After categorizing the comments, it will be defined as the set of explicit comments. After an explicit comment set is defined, sentiment analysis is performed to systematically identify the interest of the customer through comments. Here the comments are classified into the polarity of given texts in an explicit comment set into positive, negative, and neutral. Finally, scores were calculated for all brands which will help to recommend the product.
Personalized Context-Aware Authentication Protocols in IoT

The IoT is a specific type of network with its own communication challenges. There are a multitude of low-power devices monitoring the environment. Thus, the need for authentication may be addressed by many available sensors but should be performed on the fly and use the personal characteristics of the device’s owner. Thus, a review and a study of the available authentication methods were performed for use in such a context, and as a result, a preliminary algorithm was proposed as a solution. The algorithm utilizes a variety of independent factors, including the user’s personal characteristics, knowledge, the context in which the authentication is taking place, and the use of steganography, to authenticate users in the dispersed environment. This algorithm encodes all of these factors into a single data vector, which is then used to verify the user’s identity or as a digital signature. Using this personalized context-aware protocol, it is possible to increase the reliability of authentication, given the emphasis on usability in low-computing-power but highly sensor-infused environments and devices. Although more testing is needed to optimize it as an industry solution, personalized protocols seem to have a future in the IoT world.
A Bibliometric Analysis of Digital Literacy Research from 1990 to 2022 and Research on Emerging Themes during the COVID-19 Pandemic

Due to the rapid advancement of digital technology and its contribution to sustainable development, digital literacy has become an increasingly significant research topic. However, the uneven distribution of new technologies has caused emerging inequalities, which have been exacerbated by the COVID-19 pandemic, one of the most significant public health crises of the century. This paper aims to conduct an in-depth analysis of scientific production using bibliometric methods to comprehend the current research status of digital literacy studies and evaluate the pandemic’s impact on such research. In total, 7523 documents published between 1990 and 2022 were identified and analyzed using bibliometric research methods in the Web of Science database. These methods included growth trend analysis, network analysis, highly cited literature analysis, factor analysis, and time-series-based analysis of frequently discussed topics. Additionally, a word cloud analysis of the keywords in digital literacy literature from 2020 to 2022, during the COVID-19 pandemic, was created. The study’s outcomes explore digital literacy research, including current trends, significant publications, and institutions involved in the field. This study emphasizes the importance of digital literacy in today’s society, particularly during the COVID-19 pandemic. It also highlights the potential of bibliometric analysis as a tool for identifying research gaps.
An Internet of Things Access Control Scheme Based on Permissioned Blockchain and Edge Computing

In the IoT (Internet of Things) environment, the existing access control schemes for device resources have some problems, such as poor scalability, high latency, security, and dynamics. Combining the advantages of the permissioned blockchain and edge computing, an access control scheme for the Internet of Things based on the permissioned blockchain and edge computing is proposed. By authenticating the user’s identity at the edge, the user’s identity is reliable and the response time is improved. In the ABAC (Attribute Based Access Control) model, the blockchain is regarded as a trusted entity, and the access control policy is written into a smart contract and deployed on the blockchain for calling. Most of the existing consensus algorithms have the problems of low throughput and scalability. A Kraft (Kademlia–Raft) consensus algorithm is introduced to solve the above issues. Security analysis and experimental results show that the scheme can achieve fine-grained, dynamic access control, has high throughput and low latency, and ensures security and reliability.
Head-Mounted Projector for Manual Precision Tasks: Performance Assessment

The growing interest in augmented reality applications has led to an in-depth look at the performance of head-mounted displays and their testing in numerous domains. Other devices for augmenting the real world with virtual information are presented less frequently and usually focus on the description of the device rather than on its performance analysis. This is the case of projected augmented reality, which, compared to head-worn AR displays, offers the advantages of being simultaneously accessible by multiple users whilst preserving user awareness of the environment and feeling of immersion. This work provides a general evaluation of a custom-made head-mounted projector for the aid of precision manual tasks through an experimental protocol designed for investigating spatial and temporal registration and their combination. The results of the tests show that the accuracy (0.6±0.1 mm of spatial registration error) and motion-to-photon latency (113±12 ms) make the proposed solution suitable for guiding precision tasks.