Reviewerly/exHarmony huggingface dataset:
BIOS-Based Server Intelligent Optimization

Servers are the infrastructure of enterprise applications, and improving server performance under fixed hardware resources is an important issue. Conducting performance tuning at the application layer is common, but it is not systematic and requires prior knowledge of the running application. Some works performed tuning by dynamically adjusting the hardware prefetching configuration with a predictive model. Similarly, we design a BIOS (Basic Input/Output System)-based dynamic tuning framework for a Taishan 2280 server, including dynamic identification and static optimization. We simulate five workload scenarios (CPU-instance, etc.) with benchmark tools and perform scenario recognition dynamically with performance monitor counters (PMCs). The adjustable configurations provided by Kunpeng processing reach 2N(N&gt;100). Therefore, we propose a joint BIOS optimization algorithm using a deep Q-network. Configuration optimization is modeled as a Markov decision process starting from a feasible solution and optimizing gradually. To improve the continuous optimization capabilities, the neighborhood search method of state machine control is added. To assess its performance, we compare our algorithm with the genetic algorithm and particle swarm optimization. Our algorithm shows that it can also improve performance up to 1.10× compared to experience configuration and perform better in reducing the probability of server downtime. The dynamic tuning framework in this paper is extensible, can be trained to adapt to different scenarios, and is more suitable for servers with many adjustable configurations. Compared with the heuristic intelligent search algorithm, the proposed joint BIOS optimization algorithm can generate fewer infeasible solutions and is not easily disturbed by initialization.
parts and the whole. Integrate knowledge

The article deals with the themes of integration between the different cataloguing and disciplinary descriptive traditions. It initially evaluates the transition phase from methods to established practices up to the new solicitations caused by the transformations of information production models. Lastly, the paper focus on the theme of the reconceptualization of domain languages ​​to move on to outline the concept of metacataloguing as a possible descriptive synthesis.
Math Trainer as a Chatbot Via System (Push) Messages for Android

In the recent years, as technology grew, so did the chatbot technology. More and more companies and people are using and reaping the benefits from them. This paper shows how a chatbot for mathematical tasks, named mathbot, was developed. The chatbot uses mathematical exercises from the “Schulapps” of the LearningLab of Graz, University of Technology and combines them with textual questions to provide a useful utility for practicing. It is shown that such a chatbot could be useful for pupils and teachers by evaluating a test run in an elementary school.
Adaptive alarm prediction in optical network based on model generalization in cross-layer AI

Artificial intelligence (AI) applications about optical network management are increasing, especially in fault diagnosis. When the network fault or alarm occurs, the equipment running status and performance parameters are changing. AI algorithms can extract the internal relationship from the data and it is proved that the alarm prediction method based on AI is effective. However, the performance of AI models, especially supervised learning, rely on large data sets. These training sets with only a small number of samples are usually unavailable due to the lack of historical data. How to improve the generalization ability of AI models is still a key problem to be solved in the intelligent maintenance of optical networks. In optical network alarm prediction, performance data of devices have different degrees of relationship to alarms and the generalization performance of models is limited by the data structure. In this paper, we propose a method to optimize the model generalization ability in alarm prediction, i.e., the training of a small sample model is solved through parameter transfer in the training process. We verify the generalization effect of the models by using real data of a commercial optical network. Experimental results show that the models achieve significant performance improvement in the data set with a high correlation between performance and alarm.
MANet: End-to-End Learning for Point Cloud Based on Robust Pointpillar and Multiattention

Detecting 3D objects in a crowd remains a challenging problem since the cars and pedestrians often gather together and occlude each other in the real world. The Pointpillar is the leader in 3D object detection, its detection process is simple, and the detection speed is fast. Due to the use of maxpooling in the Voxel Feature Encode (VFE) stage to extract global features, the fine-grained features will disappear, resulting in insufficient feature expression ability in the feature pyramid network (FPN) stage, so the object detection of small targets is not accurate enough. This paper proposes to improve the detection effect of networks in complex environments by integrating attention mechanisms and the Pointpillar. In the VFE stage of the model, the mixed-attention module (HA) was added to retain the spatial structure information of the point cloud to the greatest extent from the three perspectives: local space, global space, and points. The Convolutional Block Attention Module (CBAM) was embedded in FPN to mine the deep information of pseudoimages. The experiments based on the KITTI dataset demonstrated our method had better performance than other state-of-the-art single-stage algorithms. Compared with another model, in crowd scenes, the mean average precision (mAP) under the bird’s-eye view (BEV) detection benchmark increased from 59.20% of Pointpillar and 66.19% of TANet to 69.91 of ours, the mAP under the 3D detection benchmark was increased from 62% of TANet to 65.11% of ours, and the detection speed only dropped from 13.1 fps of Pointpillar to 12.8 fps of ours.
Lightweight Real-Time Image Semantic Segmentation Network Based on Multi-Resolution Hybrid Attention Mechanism

Effective perception of the surrounding environment and the balance between accuracy and processing speed are crucial for the successful application of real-time semantic segmentation algorithm in the fields of autonomous driving, drones, and smart security. In this paper, a lightweight feature reuse network MHANet for real-time semantic segmentation is proposed. The main novelties of our method are improved ResNet and attention-based fusion mechanism. And the effectiveness of our method is verified by a large number of experiments. Without any pre-training process, the performance of real-time segmentation is improved by using deep fusion of segmentation maps with different resolutions. At the same time, our network converges faster than other networks using pre-training when trained from scratch. Compared with existing methods, the results obtained with our method on the Camvid dataset improve in accuracy (mIoU) ranging from 2% to 6% and in efficiency (FPS) ranging from 15% to 18%. The results achieved 71.87% mIoU of accuracy in the Cityscapes test set, processing images at 203 FPS. Experiments show that manual designed MHANet is effective in improving the performance of real-time semantic segmentation without any pre-training.
Investigation of 6-Year Land Use Change of Sakarya River Around İnhisar (Bilecik) using Remote Sensing

İnhisar (Bilecik) is located in the center-east of the study area, which is bounded by Söğüt (Bilecik) in the southwest and Yenipazar (Bilecik) in the northeast. Sakarya River, located in the center of the study area, provides continuity in the East-West direction for approximately 40 km and is fed by drains from both the north and the south. Marble enterprises with economic value are widely observed in the region. In addition, it is very important for the natural environment to protect areas such as İnhisar waterfall and Harmankaya Canyon Nature Park in the region from harm that may be caused by human activities. The main purpose of the study is to examine the areas with natural structures that are important for the environment on a regional scale with data provided by earth observation satellites and to define the time-dependent changes of these areas. Landsat-8 satellite images, a 12.5m-resolution digital elevation model and geology map constitute the main data of the study. The results of the change analysis conducted on the Landsat images classified with the random forest algorithm are discussed on the geology and topography maps. When the 6-year change analysis findings are interpreted, it is suggested that the mining activities, which intensified especially around Gölpazarı, should not approach the Yenipazar-İnhisar line in the east.
Rural Architectural Planning and Landscape Optimization Design under the Background of Ecological Environment Protection

The ecological problems faced by China's environmental protection are becoming more and more serious. Serious haze occurs frequently in some areas. Water pollution, soil pollution, and other new types of pollution are still relatively prominent problems. Therefore, rural architectural planning and landscape optimization design should be based on the premise of ecological environmental protection. This paper puts forward the evaluation of rural architectural planning and landscape in the context of ecological environment protection and uses the analytic hierarchy process to analyze and obtain the evaluation results. This method has a comprehensive and scientific powerful evaluation function. The experimental results of this paper show that after the evaluation of the analytic hierarchy process, it is found that the comprehensive score of the architectural planning and landscape of village A is not very high. The highest weight is 0.3210, the landscape diversity score of street A is 1.28, and the landscape diversity score of street D is 1.76. This is the highest score, indicating that the architectural planning and landscape of the village cannot meet the needs of contemporary ecological environmental protection. Aiming at the problems existing in the landscape, the corresponding measures are also given at the end of the experiment, which has certain significance for the landscape optimization design.
Multi-Vehicle System Localization by Distributed Moving Horizon Estimation over a Sensor Network with Sporadic Measurements

This paper proposes a Distributed Moving Horizon Estimator (DMHE) for the Multi-Vehicle system localization problem using Sensor Networks with sporadic measurements. Due to its capability to efficiently exploit environmental information via constraints, the proposed DHME technique is well-suited to better estimate the system state when measurements are available at time instants a priori unknown. Indeed, the use of output constraints can contribute to locally improve estimation accuracy, especially when dealing with sporadic measurements and biased sensors data. A realistic case study is proposed within the Robot Operating System framework and Gazebo to localize a Multi-Vehicle System using an inexpensive Sensor Network with low-computation capabilities. A comparative campaign simulation is performed to confirm the effectiveness of the proposed DMHE algorithm in terms of accuracy, computation time, and constraints handling with respect to existing results.
Influences of Digital Twin Technology on Learning Effect

This study designed a questionnaire to investigate the influences of digital twin (DT) technology on ubiquitous learning effect based on the relevant theory.The influences of four subsystems (virtual-reality symbiotic system, information transmission system, intelligent control system and teaching support system) of DT technology on ubiquitous learning effect were analyzed.Results demonstrate that the overall Cronbach α of the designed questionnaire is 0.850, KMO is 0.785, and the corresponding P-value is 0.000, indicating the good reliability and validity of the questionnaire.All four subsystems (information transmission system > teaching support system > intelligent control system > virtual-reality symbiotic system) of DT technology have significantly positive influences on the ubiquitous learning effect.The obtained conclusions have important references to construct personalized DT learning space, facilitate construction of digital campus, accelerate deep integration of intelligent technologies into education reform, and promote personalized learning of learners.
IoT-Based Access Management Supported by AI and Blockchains

Internet-of-Things (IoT), Artificial Intelligence (AI), and Blockchains (BCs) are essential techniques that are heavily researched and investigated today. This work here specifies, implements, and evaluates an IoT architecture with integrated BC and AI functionality to manage access control based on facial detection and recognition by incorporating the most recent state-of-the-art techniques. The system developed uses IoT devices for video surveillance, AI for face recognition, and BCs for immutable permanent storage to provide excellent properties in terms of image quality, end-to-end delay, and energy efficiency.
STRATEGIC READING INTERVENTION FOR LEFT-BEHIND LEARNERS IN THE PHILIPPINES

Education for all means no one should be left behind; thus, teachers should provide effective strategies for struggling readers. The “one-size fits all strategy” further divides the gap between the slow and advanced learners. This study used a quantitative observational research design to determine the reading ability of 30 struggling readers in terms of word recognition, reading comprehension, and reading speed using reading stories, Dolch's basic sight words and the Phil-IRI Manual 2018. The study develops strategic reading intervention materials to support teachers and students during the remedial programs. The pre-tests revealed that the reading ability of the struggling readers was at a frustration level. Furthermore, the develop strategic intervention reading materials used in remedial programs were composed of 4 parts: learning content, learning task guide, assessment guide, and enhancement guide. The post-tests show that the participants reading ability progressed to instruction and independent level after the remedial program. It was concluded that determining the student’s present ability is beneficial to develop effective intervention materials. It was suggested that the school leaders and program specialists should develop faculty and student support programs ideally to uplift the student’s literacy.
Algorithm of Encrypting Digital Image Using Chaos Neural Network

With the rapid development of digital communication technology, data and image encryption methods will get more and more in-depth research in these aspects. Although the original cipher method can keep the image confidential, because of the storage method of the image data, for the image with too high pixel value, the process of keeping the two-dimensional information confidential and decoding will bring a large amount of computation, and the accuracy of encryption and decryption cannot be guaranteed. This paper presents a visual image encryption algorithm based on the compressed sensing method of the Hopfield chaotic neural network. Different from the existing visual image encryption methods, our method can directly embed the noise like information after encryption into the alpha channel of the carrier image, thus increasing the visual stability of the computer. Finally, through the comparison and analysis of simulation experiments, we find that the image encryption algorithm proposed in this paper has the advantages of wide password space, low risk, good visual stability, good decoding effect, and good robustness.
Job Matching Analysis Based on Text Mining and Multicriteria Decision-Making

The postmanagement of an enterprise is to rationally and scientifically arrange the talents who meet the postrequirements and are suitable for the development of the enterprise in their suitable posts through testing, selection, and employment. The job recruitment information published on the recruitment website is the data that best reflects the market demand for data analysis talents. However, as online recruitment information is mostly presented in the form of text, this paper explores the information contained in the recruitment information through text mining technology, which has reference value for data analysis job seekers. Based on text mining and multicriteria decision-making, this paper studies job matching. The technical jobs under text mining are mainly distributed in the Internet industry, and the number of recruits accounts for 66.45%. Other industries have less demand for technical data analysis, all of which are below 15%. However, the market of business data analysis is scattered and extensive, and education still occupies the largest demand provider, accounting for 30.53%. However, real estate and media are closely followed, accounting for 24.28% and 22.34%, respectively. Besides these three industries, compared with technical talents, service industries will also need business data analysis talents more. In the stage of data collection, through the data analysis of the recruitment website, the job recruitment information is crawled, as well as other job information such as job salary, job qualifications, work experience requirements, and the number of recruits. This paper carries out the data collection stage and crawls the job recruitment information through the data analysis of the recruitment website. And, other position information, such as position salary, position qualification, and work experience requirements, as well as the number of recruits. The research of this paper can help the data released by the recruitment website to analyze the recruitment information and quickly reflect the market demand data for data analysis talents.
A Diagnostic Model of Breast Cancer Based on Digital Mammogram Images Using Machine Learning Techniques

Breast cancer disease is one of the most recorded cancers that lead to morbidity and maybe death among women around the world. Recent research statistics have exposed that one from 8 females in the USA and one from 10 females in Europe are contaminated by breast cancer. The challenge with this disease is how to develop a relaxed and fast diagnosing method. One of the attractive ways of early breast cancer diagnosis is based on the mammogram images analysis of the breast using a computer-aided diagnosing (CAD) tool. This paper firstly aimed to propose an efficient method for diagnosing tumors based on mammogram images of breasts using a machine learning approach. Secondly, this paper aimed to the development of a CAD software program for breast cancer diagnosing based on the proposed method in the first step. The followed step-by-step procedure of the proposed method is performed by passing the Mammographic Image Analysis Society (MIAS) through five steps of image preprocessing, image segmentation using seeded region growing (SRG) algorithm, feature extraction using different feature’s extraction classes, and important and effectiveness feature selection using the Sequential Forward Selection (SFS) technique, and finally, the Support Vector Machine (SVM) algorithm is used as a binary classifier in two classification levels. The first level classifier is used to categorize the given image as normal or abnormal while the second-level classifier is used for further classifying the abnormal image as either a malignant or benign cancer. The proposed method is studied and investigated in two phases: the training phase and the testing phase, with the MIAS dataset of mammogram images, using 70% and 30% ratios of dataset images for the training and testing sets, respectively. The practical implementation of the proposed method and the graphical user interface (GUI) CAD tool are carried out using MATLAB software. Experimental results of the proposed method have shown that the accuracy of the proposed method reached 100% in classifying images as normal and abnormal mammogram images while the classification accuracy for benign and malignant is equal to 87.1%.
A novel activation function based recurrent neural networks and their applications on sentiment classification and dynamic problems solving

In this paper, a nonlinear activation function (NAF) is proposed to constructed three recurrent neural network (RNN) models (Simple RNN (SRNN) model, Long Short-term Memory (LSTM) model and Gated Recurrent Unit (GRU) model) for sentiment classification. The Internet Movie Database (IMDB) sentiment classification experiment results demonstrate that the three RNN models using the NAF achieve better accuracy and lower loss values compared with other commonly used activation functions (AF), such as ReLU, SELU etc. Moreover, in terms of dynamic problems solving, a fixed-time convergent recurrent neural network (FTCRNN) model with the NAF is constructed. Additionally, the fixed-time convergence property of the FTCRNN model is strictly validated and the upper bound convergence time formula of the FTCRNN model is obtained. Furthermore, the numerical simulation results of dynamic Sylvester equation (DSE) solving using the FTCRNN model indicate that the neural state solutions of the FTCRNN model quickly converge to the theoretical solutions of DSE problems whether there are noises or not. Ultimately, the FTCRNN model is also utilized to realize trajectory tracking of robot manipulator and electric circuit currents computation for the further validation of its accurateness and robustness, and the corresponding results further validate its superior performance and widespread applicability.
Prompt-Based Generative Multi-label Emotion Prediction with Label Contrastive Learning

Multi-label emotion prediction, which aims to predict emotion labels from text, attracts increasing attention recently. It is ubiquitous that emotion labels are highly correlated in this task. Existing state-of-the-art models solve multi-label emotion prediction in sequence-to-sequence (Seq2Seq) manner, while such label correlations are merely leveraged in decoding side. In this work, we propose an emotion prediction framework to jointly generate emotion labels and template sentences via Seq2Seq language model. On the one hand, our template-based natural language generation method makes better use of generative language model compared with generating label sequences in the prior Seq2Seq-based generative classification model. On the other hand, we introduce the Correlation-based Label Prompts (CLP) through soft prompt learning and contrastive learning, which enables our model to further consider emotion label correlations in encoding side. To demonstrate the effectiveness of our prompt-based generative multi-label emotion prediction model, we perform experiments on the GoEmotions and SemEval 2018 datasets, achieving competitive results, outperforming 7 baselines w.r.t. 3 evaluation metrics. In-depth analyses show the generation manner is much more impressive compared with generating label sequences and our model is particularly effective in label correlation modeling.