Reviewerly/exHarmony huggingface dataset:
The Effectiveness of Using Padlet in Collaborative Writing Based on Students’ Perceptions

The phenomena of technological growth in English instruction are expanding quickly all over the world.This progress happens not just in wealthy nations, but also in emerging nations, where the instructor mustdevelop technology-based teaching strategies and tools when teaching English. Indonesia, a developingnation, places a strong emphasis on expanding the use of technology in EFL instruction. English wassubsequently included in the general curriculum and even taught to kids in elementary schools as a directresult of this choice. This study will use Padlet as a digital tool that may be used in a writing class topique kids’ interest in learning English, especially in a writing class. Additionally, this study intends topromote the use of collaborative writing to make it simpler for elementary school pupils to use Padlets inwriting lessons. Through the use of qualitative research techniques, this study set out to learn more abouthow elementary school kids perceived using Padlets for group writing projects. A total of 52 elementaryschool kids in grades 4 and 5 participated in this study. We conducted semi-structured interviews toget data from participants. After the data has been collected, it will be processed using various contentanalysis techniques. Researchers received feedback from students who claimed that the usage of Padlets inEFL classes, particularly in collaborative writing, produced several themes. These themes include thatusing Padlet in collaborative writing can increase students’ self-confidence, that Padlet also helps studentsimprove their fluency in writing, and that Padlet is easy to use, has attractive features, and is simple tooperate.
Predicting cancer outcomes from whole slide images via hybrid supervision learning

Collaboratively leveraging limited pixel-level segmentation annotations and large-scale slide-level classification labels in hybrid supervision learning can significantly enhance model performance. However, the direct application of this approach within computational pathology presents challenges as end-to-end classification models grapple with processing high-resolution whole slide images (WSIs). An alternative approach is to use patch-based models with pixel-level pseudo-labels, but these models can be susceptible to the cumulative effects of noisy labels, leading to convergence and drift problems during iterative training. To surmount these hurdles, we propose a hybrid supervision learning framework tailored for pathological image classification. Our method employs coarse classification labels to optimize pixel-level pseudo-labels and incorporates a comprehensive strategy to diminish false negatives and positives throughout the segmentation process. This framework holistically integrates the supervised information derived from segmentation and classification procedures, and is applicable to the general classification of high-resolution images, thereby boosting both specificity and sensitivity. We assess our proposed method’s effectiveness using one publicly accessible dataset and two proprietary datasets, collectively constituting over 10,000 pathological images of various disease types such as gastric, cervical, and breast cancer. Our experimental results reveal a 100% sensitivity rate in slide-level classification tasks, simultaneously reducing the false-positive rate to a mere third of the state-of-the-art. In conclusion, this paper presents a potent instrument for the precise and efficient classification of high-resolution pathological images, with promising results showcased across a wide array of datasets and disease types. Code is available at https://github.com/JarveeLee/HybridSupervisionLearning_Pathology.
Restricted orthogonal gradient projection for continual learning

Continual learning aims to avoid catastrophic forgetting and effectively leverage learned experiences to master new knowledge. Existing gradient projection approaches impose hard constraints on the optimization space for new tasks to minimize interference, which simultaneously hinders forward knowledge transfer. To address this issue, recent methods reuse frozen parameters with a growing network, resulting in high computational costs. Thus, it remains a challenge whether we can improve forward knowledge transfer for gradient projection approaches using a fixed network architecture. In this work, we propose the Restricted Orthogonal Gradient prOjection (ROGO) framework. The basic idea is to adopt a restricted orthogonal constraint allowing parameters optimized in the direction oblique to the whole frozen space to facilitate forward knowledge transfer while consolidating previous knowledge. Our framework requires neither data buffers nor extra parameters. Extensive experiments have demonstrated the superiority of our framework over several strong baselines. We also provide theoretical guarantees for our relaxing strategy.
Constructing high-resolution stratigraphic frameworks by the application of signal analysis techniques: Example of Balbuena IV sequence, Yacoraite formation, Salta Basin

One of the most essential and valuable applications of high-resolution sequence stratigraphy is reservoir zonation and characterization. Establishing a hierarchical chronostratigraphic framework comprising sequences at multiple scales is crucial to identify main reservoir heterogeneities that compartmentalize the fluid flow in the subsurface. In this context, this paper proposes a workflow that integrates different signal analysis techniques to identify sequences of multiple hierarchies from a series of stratigraphic data. The Continuous Wavelet Transform (CWT), Detrend Error Log (DTEL), and Integrated Detrend Error Log (INDTEL) techniques are utilized to process gamma-ray data obtained from outcrops of the Yacoraite Formation, which are conventionally employed as analogs for reservoir characterization. Our results suggest that CWT fits better with higher frequency cycles, while INDTEL shows a good fit with medium frequency cycles. In addition to these techniques, Hidden Markov Models were also applied, predicting T-R cycles as hidden states from the facies transition matrix and possible occurrence of missed beats. All these methods were scripted in the Python programming language, enabling the generation of fast and interactive outputs. This approach brings parameters that can guide the construction of stratigraphic models by automated processes.
Investment decisions and passive portfolio construction utilizing patent analytics: A multi-case study on COVID-19 treatment technologies

The question of which stocks to buy remains at the heart of every investment decision. New, non-priced information sources and rationales linking such information to financial theory are constantly investigated. Patent metrics are often relied upon to capture technological change. Prior research detected positive corporate performance implications when relating corporate patent metrics to corporate financial performance indicators like stock prices. However, largely anecdotal evidence from the corporate sector on building investment strategies based on such patent metrics exists. In this multi-case study comprising COVID-19 treatment technologies, we select investment targets from a pool of companies previously identified based on patent analytics via patent portfolio sizes, average patent qualities, and patent portfolio strengths. Casting our thematic investment choice into passively constructed portfolios and benchmarking the portfolios' performance against the sector and three established global benchmarks, we find that relying on patent information in the selection process results in substantial outperformance compared to these global benchmarks. This finding holds true in the short- and mid-term. These findings provide ample opportunities for future research and may guide investment, managerial, and policy decision-makers in crafting promising investment strategies.
Unsupervised statistical image segmentation using bi-dimensional hidden Markov chains model with application to mammography images

Hidden Markov chain (HMC) models have been widely used in unsupervised image segmentation. In these models, there is a double process; a hidden one noted X and an observed one, which is often one-dimensional, noted Y. The latter is constituted by pixels of a noisy image after transforming its bi-dimensional form into a mono-dimensional sequence. In this context, these models run into a problem of relationships between pixels which is often solved by applying curves such as the Hilbert-Peano scan when modeling the image under study. We propose enriching the HMC model by introducing a second component to the observed process Y based on the average of two observations which are neighbors in the image but are not in the chain of each considered pixel. This gives a bi-dimensional HMC model which has the same structure as the classical model except for the two-dimensional case of the low modeling noise. The estimation of the parameters of this model is carried out by using a three-algorithm approach: Bayesian one based mainly on the Markov Chain Monte Carlo (MCMC) methods, Expectation-Maximization (EM), and Iterative Conditional Estimation (ICE). We apply the final Bayesian decision criteria Marginal Posterior Mode to come up with a final configuration of the result X. The proposed model is compared to the classical HMC model in combination with the Hilbert-Peano scan numerically through simulated data and visually through synthetic and mammogram images.
What a waste – A norm-critical design study on how waste is understood and managed

This article seeks to contribute new insights into inclusive recycling environments through universal design. The aim is to develop knowledge on how waste is understood and managed by exploring the practice and design of a recycling environment. Applying a norm-critical view, this is explored in a study with participants from a municipal housing company and waste management company in northern Sweden. Methods used are go-along interviews together with professionals and observations of a recycling environment. The results are discussed as: 1) the "(un)social norm;" where our results show that the waste system is perceived as a social system, contradicting previous approaches where waste systems are treated as technical environments. 2) the "(un)design factor;" where we identify how design in a recycling context that doesn´t emphasise diversity may affect activities of sorting.
DESC: Descriptors Extractor for Shape Contours

Trajectory descriptions for shape outlines are a common way for image or shape representations. In this paper, we present our research results and associated software code to extract bag-of-words based trajectory descriptions for shape contours or outlines. Our algorithm can generate multiple distinct representations for the same outline to accommodate writing variations and capture local features at stroke-segment level. The extracted descriptions can be utilized in many applications, including template matching for handwriting recognition/correction, writer identification, signature verification, etc.
Distributed Human Pose Estimation Based on BlazePose

Abstract The lightweight of human pose estimation model is one current research hotspot in the field of computer vision. The emphasis of current research is to promise lightweight model while improving the network performance. In view of computing and processing performance of monocular static image pose estimation method deployed in mobile devices, a distributed human pose estimation method was proposed. The distributed framework was constructed by simplifying BlazePose as the core, and the 5×5 convolution kernels were run for feature downsampling in a multi-thread simulated distributed environment. The computing power output of mobile devices was reduced by distributed simulation. Experimental results showed that the proposed model had 3%~11% performance improvement compared with other models in the simulation environment. Compared with the traditional human pose estimation model, the proposed model reconstructs the neural network deployment framework, thus indirectly enabling stable recognition of the deployed equipment under low computing power.
Scientific analysis of diagnostics of heating of solar panels and methods of their cooling

Abstract Today, the share of using alternative energy sources has become possible thanks to the study of the potential of a particular geographic area. In many countries, solar and wind energy have become a promising source. The study of a solar energy potential in the conditions of Turkmenistan showed that the potential of a solar radiation throughout the entire territory reaches 700-800 W/m 2 , and this is a great opportunity to use a solar energy. In the work, an experimental and theoretical study of the efficiency of the solar panel water cooling system in the hot climate of Turkmenistan was carried out. Since the climate of Turkmenistan is sharply continental, in summer the ambient temperature exceeds +40 °C. As a result of the research, the authors found that with an increase in the ambient temperature, the surface temperature of the solar panels also increases, which as a result leads to a decrease in its performance by 20-25%. The developed solution made it possible to reduce the surface temperature of solar panels in the hot climatic conditions of Turkmenistan and increase the current at 1200 hours of the day by 25%, at 1300 by 62%, at 1400 by 58% and at 1500 by 33.3%, to increase power by 19%, 42%, 38%, 18%, respectively, while 2.8, 3.3, 3.9, 5.4, 5.4, 4.9, and 3.2 kW of thermal energy were taken.
DCMigrationALG: A Power-Aware Data Center Container Migration Algorithm

Abstract In order to make full use of the power capacity of the data center when deploying servers in the data center, the deployment method of data center power over-provisioning is adopted. By using the hypothetical power capacity of the data center instead of the actual power capacity to deploy servers, we can deploy more servers to improve power utilization and space utilization. However, in some cases, when the servers under the same power supply component reach a higher power level at the same time, the real-time power of the power supply component will exceed the rated power, affecting service operation and causing heavy losses. In response to the above problems, we propose DCMigrationALG: a power-aware data center container migration algorithm. This algorithm uses container migration to migrate the containers under the power supply component to other power supply components, thereby reducing the real-time power of the power supply component and ensuring power security. We conduct experiments in a real computer room environment to prove the effectiveness of our algorithm.
Improvement of Remote sensing image target detection algorithm based on YOLO V5

Abstract YOLO V5 algorithm is the current target detection algorithm, but the accuracy of target boundary regression is relatively low, and it cannot be used in scenarios with high requirement of prediction frame intersection ratio. Therefore, this paper proposes an improved YOLO V5 algorithm with high accuracy of target frame and fast convergence of model. The algorithm can avoid the loss of shallow semantic information, deepen the pyramid depth and increase the detection layer by using cross-hierarchy method and multiple feature fusion. By reducing network computation and parameter number, network lightweight processing is realized, and real-time performance loss caused by model complexity is avoided.
Research on Aircraft Wheel Hub Pose Detection Method Based on Machine Vision

Abstract In order to achieve the action of lifting and clamping the aircraft wheel by the wheel holding device, machine vision is innovatively introduced to detect the position and pose of the wheel hub during the wheel holding process. The overall process design was systematically carried out, and an industrial camera was selected and calibrated. Then, the generalized Hough transform was used to extract the target from the processed image, and its center position was calculated. Based on the data of the number and position of the target’s center, it was determined whether the wheel hub was intact. If it was incomplete, more photos were taken. If the number of targets met the standard and the wheel hub was intact, the PnP pose calculation algorithm was adopted to calculate the position and pose of the wheel hub. The results showed that the calculated position and pose met the requirements proposed by the project design.
Vehicle Detection Using the Light Weight Model Based on YOLOv5

Abstract Vehicle detection is important for the administration because it’s an important part of the intelligent traffic management system. It can help to make the best decision for the government and provide the data for intelligent roadside equipment. And can reduce road congestion and emissions, reduce the incidence of traffic accidents, improve road safety and reduce damage caused by accidents. The current method of object detection needs large computing resources and makes the roadside equipment costly or unable to meet the computing resources. So, this paper proposed a light weight detection model based on YOLOv5. This model reduces the parameter and FLOPs by using the deepthwise separable convolution CBAM to improve the performance of vehicle detection. And this paper reduces the input image size and halves the feature channel to reduce the amount of computation. The experiment result shows that the proposed model is 8 times smaller than YOLOv5-s while the mAP only decreases 6% on the UA-DETRAC dataset, significantly outperforming the other methods. This paper provided an idea for implementing a light weight model and a way to reduce the application cost of roadside smart devices.
Research on an Improved Adaptive Image Enhancement Algorithm

Abstract Aiming at the image degradation problems that occur during industrial activities and the shortcomings of traditional fuzzy enhancement algorithms in dealing with image degradation problems with high algorithm complexity and poor enhancement results. Based on the tuned tri-threshold fuzzy intensification algorithm, this paper proposes an adaptive image enhancement algorithm, which combines three kinds of degraded images in reality (i.e., images in dusty, night and foggy environments) for simulation verification, and compares the visual effects of the enhanced images and makes objective quantitative evaluation. The experimental results show that: the proposed algorithm can not only effectively improve the contrast of the image, keep the detailed information of the image, and make the image present a more natural visual effect, but also improve the quality evaluation index of the image, and make the variance value is improved by more than 5% compared with other comparison algorithms.
System Health Assessment Model Based on Anomaly Detection

Abstract Accurately assessing system health and taking timely interventions play a crucial role in promoting intelligent system operation. Traditional system health assessment techniques depend on human-set thresholds, which are only appropriate for tracking a few indicators and do not accurately represent the system’s health overall. Therefore, keeping track of a large amount of data and a diverse variety of indicators will be extremely challenging. To address this issue, we develop the AD_Select anomaly detection algorithm and suggest a system health assessment model based on indicator anomaly detection. By fusing anomaly detection with system health assessment, this algorithm increases the scientific rigor and precision of health assessment. The experiments of AD_Select anomaly detection algorithm and healthiness assessment model are conducted on the 2018AIOps dataset and industrial dataset respectively, and the results show that both the AD_Select algorithm and the healthiness assessment model proposed in this paper achieve good performance.
Security and privacy problems in voice assistant applications: A survey

Voice assistant applications have become omniscient nowadays. Two models that provide the two most important functions for real-life applications (i.e., Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR) models and Speaker Identification (SI) models. According to recent studies, security and privacy threats have also emerged with the rapid development of the Internet of Things (IoT). The security issues researched include attack techniques toward machine learning models and other hardware components widely used in voice assistant applications. The privacy issues include technical-wise information stealing and policy-wise privacy breaches. The voice assistant application takes a steadily growing market share every year, but their privacy and security issues never stopped causing huge economic losses and endangering users' personal sensitive information. Thus, it is important to have a comprehensive survey to outline the categorization of the current research regarding the security and privacy problems of voice assistant applications. This paper concludes and assesses five kinds of security attacks and three types of privacy threats in the papers published in the top-tier conferences of cyber security and voice domain.
Graph Transformation Based on Heterogeneous Information Network for Graph Algorithms

Abstract In the finical risk control, the graph structure data increasingly show its unique charm, especially the heterogeneous information network (HIN). And, graph computing algorithms for the data mining based on network is the most popular way at the moment. But, most of them require the graph network must have homogeneity and could not be applied to heterogeneous networks. Although the neural network models have some work in the HIN, which like heterogeneous graph neural networks, but these ways cannot provide enough interpretability due to its operations in black-box. In this paper, we summarize strategies to transform the HIN into homogeneous network. And we propose methods aim to rebuild HIN as several homogeneous networks at fine-grained level and greatly retain the original network topological structure information compared the previous which easy to lose sight of. The effectiveness of our method is verified by real data and community detection algorithms. In the experiment, the analysis found that our approached consistently perform promising results compared with the coarse-grained data processing on HIN.
Research on Evolvable Hardware Based on Improved Evolutionary Strategy

Abstract Evolvable hardware (EHW) attracts a lot of researchers because of its excellent adaptive and self-organizing ability. Various of studies have been carried out to improve the evolutionary efficiency. There are mainly two ways: improving the structure of reconfigurable hardware and optimizing evolutionary algorithms. In this paper, we design the reconfigurable circuit based on the systolic array which could reduce the length of circuit coding. And then, an improved evolutionary strategy is proposed, which refers to the yin-yang pair optimization algorithm (YYPO). It can accelerate the evolutionary process and avoid the stalling effect by balancing the relationship between exploration and development. Some experiments were conducted to verify the effect and the results show that the improved method has better searching ability than the traditional evolutionary strategy.