Reviewerly/exHarmony huggingface dataset:
Visualizing Runtime Evolution Paths in a Multidimensional Space (Work In Progress Paper)

Runtime data of software systems is often of multivariate nature, describing different aspects of performance among other characteristics, and evolves along different versions or changes depending on the execution context. This poses a challenge for visualizations, which are typically only two- or three-dimensional. Using dimensionality reduction, we project the multivariate runtime data to 2D and visualize the result in a scatter plot. To show changes over time, we apply the projection to multiple timestamps and connect temporally adjacent points to form trajectories. This allows for cluster and outlier detection, analysis of co-evolution, and finding temporal patterns. While projected temporal trajectories have been applied to other domains before, we use it to visualize software evolution and execution context changes as evolution paths. We experiment with and report results of two application examples: (I) the runtime evolution along different versions of components from the Apache Commons project, and (II) a benchmark suite from scientific visualization comparing different rendering techniques along camera paths.
On the Acceleration of FaaS Using Remote GPU Virtualization

Serverless computing and, in particular, Function as a Service (FaaS) has introduced novel computational approaches with its highly-elastic capabilities, per-millisecond billing and scale-to-zero capacities, thus being of interest for the computing continuum. Services such as AWS Lambda allow efficient execution of event-driven short-lived bursty applications, even if there are limitations in terms of the amount of memory and the lack of GPU support for accelerated execution. To this aim, this paper analyses the suitability of including GPU support in AWS Lambda through the rCUDA middleware, which provides CUDA applications with remote GPU execution capabilities. A reference architecture for data-driven accelerated processing is introduced, based on elastic queues and event-driven object storage systems to manage resource contention and GPU scheduling. The benefits and limitations are assessed through a use case of sequence alignment. The results indicate that, for certain scenarios, the usage of remote GPUs in AWS Lambda represents a viable approach to reduce the execution time.
Towards Novel Statistical Methods for Anomaly Detection in Industrial Processes

This paper presents a novel methodology based on first principles of statistics and statistical learning for anomaly detection in industrial processes and IoT environments. We present a 5-level analytical pipeline that cleans, smooths, and eliminates redundancies from the data, and identifies outliers as well as the features that contribute most to these anomalies. We show how smoothing can make our methodology less sensitive to short-lived anomalies that might be, e.g., due to sensor noise. We validate the methodology on a dataset freely available in the literature. Our results show that we can identify all anomalies in the considered dataset, with the ability of controlling the amount of false positives. This work is the result of a research project co-funded by the Tuscany Region and a company leader in the paper and nonwovens sector. Although the methodology was developed for this domain, we consider here a dataset from a different industrial sector. This shows that our methodology can be generalized to other contexts with similar constraints on limited resources, interpretability, time, and budget.
Using Reinforcement Learning to Control Auto-Scaling of Distributed Applications

Modern distributed systems can benefit from the availability of large-scale and heterogeneous computing infrastructures. However, the complexity and dynamic nature of these environments also call for self-adaptation abilities, as guaranteeing efficient resource usage and acceptable service levels through static configurations is very difficult.
Large-scale Graph Processing and Simulation with Serverless Workflows in Federated FaaS

Serverless computing offers an affordable and easy way to code lightweight functions that can be invoked based on some events to perform simple tasks. For more complicated processing, multiple serverless functions can be orchestrated as a directed acyclic graph to form a serverless workflow, so-called function choreography (FC). Although most famous cloud providers offer FC management systems such as AWS Step Functions, and there are also several open-source FC management systems (e.g., Apache OpenWhisk), their primary focus is on describing the control flow and data flow between serverless functions in the FC. Moreover, the existing FC management systems rarely consider the processed data, which is commonly represented in a graph format. In this paper, we review the capabilities of the existing FC management systems in supporting graph processing applications. We also raise two key research questions related to large-scale graph processing using serverless computing in federated Function-as-a-Service (FaaS). As part of the Graph-Massivizer project, funded by the Horizon Europe research and innovation program, we will research and develop (prototype) solutions that will address these challenges.
Design-time Performability Evaluation of Runtime Adaptation Strategies (Work In Progress Paper)

Performability is the classic metric for performance evaluation of static systems in case of failures. Compared to static systems, Self-Adaptive Systems (SASs) are inherently more complex due to their constantly changing nature. Thus software architects are facing more complex design decisions which are preferably evaluated at design-time. Model-Based Quality Analysis (MBQA) provides valuable support by putting software architects in a position to take well-founded design decisions about software system quality attributes over the whole development phase of a system. We claim that combining methods from MBQA and established performability concepts support software architects in this decision making process to design effective fault-tolerant adaptation strategies. Our contribution is a model-based approach to evaluate performability-oriented adaptation strategies of SAS at design-time. We demonstrate the applicability of our approach by a proof-of-concept.
INFORMATION ACTIVITY AS A COMPONENT OF SCIENCE DEVELOPMENT

Common barley (lat.Hordéum vulgáre) has long been an important fodder and food crop.This is due to the fact that barley grain is the most balanced in terms of amino acid composition and is not processed by the main cereal crops, and it contains more lysine than corn, oats, sorghum, wheat or rice.In recent years, in modern agricultural production, there has been a steady trend towards increasing the use of various types of mineral fertilizers, which is due to the preservation of the balance of nutrients and soil fertility and the increase in crop yield indicators [1].Barley is one of the most common grain crops in Ukraine and the world [2].Winter and spring barley are grown on the territory of our state.In 2017, the sown area under winter barley amounted to 1619.2 thousand hectares, which is less compared to 2016 -1939.0thousand hectares, but for now there is a demand for Ukrainian barley [3].It is one of the most common grain ear crops in the world and in Ukraine.It is used in the processing, food, brewing, confectionery, and pharmaceutical industries.And in the field of fodder production, it is simply irreplaceable, because the production cost of barley grain is much lower than that of other grain crops.Barley grain is valuable for feeding animals, also because its nutritional qualities approach the standards of final feed.Field experiments were conducted during 2020-2022 at the research sites of the Department of Agrochemistry and Soil Science of the Uman National University of Science and Technology.The Komandor spring barley variety was sown.Spring barley is one of the main fodder and food crops of Ukraine.In the conditions of modern market relations, the yield and quality of grain of this crop play an important role both for farmers and for the country as a whole.Untimely application of mineral fertilizers or their excessive amount often leads to laying down of cereal plants, and this, in turn, prevents obtaining high yields, reduces the effectiveness of fertilizers, complicates the harvesting process, and worsens the quality indicators of grain [4, 5, 6, 7].The development of the total biomass of spring barley has a direct dependence on the height of the plants by development phase.In the initial phases of development (before emergence into the tube), the height of the barley plants had approximately the same indicators for the years of research and for the variants of the experiment.
A lightweight vehicle detection algorithm combining image enhancement and multi-scale feature fusion (Withdrawal Notice)

In order to solve the problem of the high rate of missed vehicle detection in night-time traffic scenarios due to insufficient illumination and variable light sources, and the high complexity of the algorithm that makes it not well suited to in-vehicle devices. In this paper, we propose an efficient and accurate night-time vehicle detection method. Firstly, an improved EnlightenGAN algorithm is proposed to enhance the vehicle features in nighttime road images. Secondly, a lightweight network MobileNet v3 is proposed to replace the original backbone network Darknet53 of YOLO v3 for feature extraction as well as a multi-scale feature fusion strategy to improve the feature extraction capability of the network. Experimental results on the ExDARK dataset show that the accuracy and recall of the detection algorithm proposed in this paper are 88.88% and 94.32%, which are 0.53% and 8% better than YOLO v3, respectively, and the computational effort is reduced by 75% compared to YOLO v3.
Research on tracking algorithm of milking robot arm based on improved SURF-KCF

Aiming at the slow movement and small displacement of dairy cows in the pasture breeding process, but the target scale changes significantly, a SURF-KCF dairy cow teat tracking algorithm is proposed. The original KCF algorithm needs to locate the target before tracking, and the tracking frame cannot be adaptive with the scale of the target. The SURF feature detection is introduced to provide target features for the KCF algorithm to achieve automatic target matching; and the scale estimation strategy is used to achieve scale adaption of cow teat targets. The experimental results show that the proposed improved algorithm achieves cow teat target matching and localization as well as scale adaption of the target area, and long-term stable tracking. Finally, in the laboratory environment, the improved algorithm is applied to the three-degree-of-freedom robotic arm automatic milking robot to realize the cow teat positioning and tracking experiment. When the cow's teat is detected, the robotic arm can automatically move to the position of the cow's teat. Good results have been obtained after testing, which shows the feasibility and effectiveness of the cow teat positioning system.
Research on construction and application of user growth system based on power industry

The user growth system based on the power industry is an important service system to build a relationship with users in this industry. This research aimed to enhance the sense of belonging of power users, improve product adhesion and achieve precise marketing goals. Taking a provincial electric power company of the State Grid (referred to as a provincial electric power company) as an example, the author built and applied a user growth system centered on power customers on the basis of data resources such as power users' flow and their electricity consumption. After analyzing the current situation and existing problems of the power user growth system, the researcher comprehensively sorted out and constructed an overall framework of this system in a targeted manner, and then applied it to the omni-channel membership center of a provincial electric power company. Finally, the application effect of the user growth system is expounded, and the future application trend is prospected.
Radar signal recognition based on time-frequency feature extraction and convolutional neural network

To solve the problem of difficult feature extraction and low recognition rate of radar signal under low signal-to-noise ratio, this paper proposes a radar signal recognition method based on time-frequency feature extraction and convolutional neural network. This method uses short-term Fourier transform (STFT) to obtain two-dimensional time-frequency images of radar signals, and then sends the images to convolutional neural networks for deep feature extraction, and realizes the classification and recognition of radar signals through convolutional neural network classifiers. The simulation results show that for different intra-pulse modulated radar signals, when the signal-to-noise ratio is -5dB, the overall recognition accuracy of the proposed model can reach more than 93%, which effectively solves the problem of low radar signal recognition rate under low signal-to-noise ratio.
Research on LatticeLSTM model based on data enhancement and self-attention mechanism

(Named Entity Recognition, NER) and (Relation Extraction, RE) are two basic tasks in Natural LanguageProcessing, NLP). Due to the indistinguishable boundaries between entities in Chinese and the lack of obvious formal signs, Named entity recognition has always been a difficult point in the Chinese field. Although it has made good progress in Chinese, it still lacks the semantic understanding ability in special fields and the effect is not ideal. In this paper, the algorithm of deep learning and self-attention mechanism are deeply studied. By improving LatticeLSTM model and integrating self-attention mechanism, the ability to understand Chinese semantics is improved, and a small amount of labeled data is expanded by data enhancement to build a data set in a special field to complete the task of named entity recognition.
Research on machine analogy translation technology based on machine learning

With the acceleration of economic globalization, the importance of translation services has gradually manifested. The machine translation technology has a long and glorious history from statistical machine translation to neural machine translation, At present, machine translation software is emerging, mostly following the rules of natural language processing. In order to achieve quality and efficiency in translation, the various ambiguities in the language should be processed accurately and the speed of translation should be increased, while the anthropomorphism of machine translation should be enhanced and the machine's knowledge of human language should be constantly improved. Therefore, it is important to strengthen the research of machine learning technology, and to insist on the research on machine analogy translation technology based on machine learning is an important research topic.
Research on technical path and practice of digital transformation of colleges based on iPaas

In 2022, the Ministry of Education officially launched the "Strategic Action for Digital Education", and digital transformation has become the focus of inward growth and transformation in the field of education. This paper first analyzed the connotation of the digital transformation of education and the difficulties of data integration encountered by colleges in the process of digital transformation, and proposed the technical path of digital transformation of colleges from two aspects: building an iPaas based educational data integration data base and key promotion strategies for digital transformation. Finally, from the perspective of practice, the article showed the practical cases of Shandong Youth Political College's digital transformation based on iPaas, with a view to providing reference for colleges to carry out digital transformation.
Research on the construction of college Chinese teaching system based on deep learning technology

With the deepening of the concept of knowledge society, the traditional college Chinese education is undergoing profound changes, which has gradually changed to "learning centered". Therefore, deep learning, core literacy and other teaching concepts have become the focus of current higher education, which has gradually been integrated with new teaching methods, such as flipped classroom and Mu class. Through multi form teaching methods, the teaching mode of information technology and curriculum integration has become the pursuit of in-depth learning in Chinese teaching, which is a learning mode of pursuing more diversified learning objectives of students. Through in-depth learning, the learning process of college Chinese will be more engaged and participatory, which will help students' cognitive processing methods, mainly including the connection of new and old knowledge, critical understanding, information integration, etc. Deep learning will increase the level of students' input and participation in the learning process, which will better develop the core quality of long-term memory and transfer of knowledge. Therefore, the construction of college Chinese teaching system based on deep learning technology is very necessary. This paper first analyzes the current research on the application of deep learning technology. Then, this paper analyzes the data analysis model of deep learning process. Finally, this paper constructs the college Chinese teaching system and puts forward some suggestions, which can better construct the teaching system.
A study on the personalized recommendation of intelligent learning system based on user feature model analysis

In the digital intelligence era, users have higher requirements for machine aided learning environment experience. How to provide personalized learning support services based on users' different characteristics has become a hot topic for researchers. Intelligent learning system (ILS) is a learning support environment that can dynamically diagnose users' different learning needs and then provide personalized services. However, the current research on intelligent learning systems is still in the exploratory stage, and the research results need to be improved in the aspect of intelligent recommendation effect. Based on this, this paper will further explore the personalized recommendation technology solution of intelligent learning system on the basis of the analysis of relevant case results. In order to improve the recommendation accuracy of intelligent learning system, we will focus on the analysis of the system architecture, feature model construction method and recommendation process from the perspective of user feature model. The simulation experiment analysis shows that the research results have certain advantages in the personalized recommendation effect, which can dynamically provide the current user with a suitable personalized learning path to meet the user's learning needs.
Service reputation assessment for power grid zero-trust security

With the development of information technology and intelligence in power grid enterprises, more and more power terminals such as smart meters and centralized collectors are being accessed remotely through various networks, especially wireless public networks, making the use of marketing terminals to launch network attacks increasing day by day. Usually, marketing terminals are deployed in uncontrolled user sites, and once deployed, they are used for long periods of time, exposing them to the serious threat of illegal counterfeit terminals and theft of legitimate terminals to attack the main power marketing system in the information intranet of the power grid enterprise. This paper proposes a zero-trust access control mechanism for power networks based on service reputation assessment, using real-time reputation values within a cycle and historical reputation values obtained from the previous test cycle to obtain a comprehensive reputation value of the service. Experimental results show that continuous trust assessment can be accurately performed based on its runtime reputation value, ensuring the continuous trustworthiness of the endpoint identity.
Reliability concept and measure of an unmanned aerial vehicle swarm

In this paper, the hierarchy and characteristics of an unmanned aerial vehicle (UAV) swarm are analyzed, and the three basic faults are presented: the entity fault, information transmission fault and process fault. Based on the causes and criteria, the concept of swarm reliability is defined, and a measurement model of mission reliability is constructed. This provides a basis for further research into UAV swarm reliability and a method for the evaluation of UAV swarm mission completion ability.
Face Recognition Application using MATLAB &amp; Python

The said application will combine the existing algorithms of face detection using MATLAB and Python programming language such as the Voila Jones algorithm and Eigenfaces algorithm, our application will then match the accuracy of several algorithms and then output the one with the best accuracy.
Two-Way Complementary Tracking Guidance

Recently, most impressive Siamese network-based trackers are equipped with two independent branches: tracked object classification and bounding box regression. However, there is no tracking information exchange between them during the tracking optimization process. This may lead to the task-mismatch and accuracy inconsistency between both classification and regression branches during inference. To tackle the problems, we propose a novel Mutual Guidance (MG) strategy for visual object tracking, which constructs the bidirectional and complementary tracking information interaction to maintain the tracked object is well-classified to also be well-localized, between classification and regression branches. Specifically, the classification branch can guide the regression one to pay more attention to the sample with high classified scores, by re-weighting the regression loss with the classification confidence. Similarity, the regression branch also guides the classifier optimization process to focus on samples with larger IoU values. And then, the proposed Mutual Guidance is completed by a series of regularization designs on classification score and regression IoU, which dynamically re-assign the adaptive weights to the losses for each sample during the joint tracking optimization. The developed MG is generic and easy to be plugged into various tracking frameworks such as anchor-based, anchor-free based and transformer based, and boost their performance to some extent with negligible additional cost. In addition, we also develop an adaptive localization(L) branch selection scheme to further assist trackers, which determines proper localization branch for different trackers according to the difference in the way of discriminating positive and negative samples. Extensive experiments verify the effectiveness of MGL and its superiority against the state-of-the-art tracking modules on OTB100, GOT-10K, LaSOT, TrackingNet, UAV123, VOT2018 and VOT2019.