Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing teamwork skills in a course on ore deposits

Abstract Teamwork has distinct pedagogic advantages and is also an important transferable skill and the dominant mode of work graduates encounter in the workplace. However, students rarely become proficient in teamwork skills during their education. The goal was to test strategies to improve teamwork skills in a course on Ores deposits, MSc Earth Science, Uppsala University. A focus on team-work was established by presenting the collaborative practicals as a series of linked opportunities to work together, get feedback on the teamwork process and reflect on and evaluate the teamwork. During the collaborative PBL sessions sociograms were used to provide feedback. Criteria based evaluation was also used to evaluate task oriented and interpersonal skills. Sociograms and criteria based evaluation promoted behavioural changes that improved teamwork interactions.
The influence of the characteristics of Internet celebrities on consumers' impulsive buying behavior in the big data economy: Empirical test based on multiple linear regression model

With the promotion of national big data policies and the development of big data technologies, traditional business models are being rewritten and entrepreneurial opportunities in big data-related fields are emerging in large numbers. web celebrity anchors have many characteristics. They can influence consumers' impulsive buying behavior through their own personality on internet, characteristics and the relationship between consumers and other characteristics. Many research results mainly focus on the web celebrity marketing mode, advantages and disadvantages, marketing strategies and the impact on consumers' purchase intention, etc., but the impact on consumers' impulsive purchase behavior is rare. The development of network technology has broken the boundaries of time and space, based on SOR theory model and big data, this paper takes consumer sentiment as a mediating variable to study the impact of personal traits of web celebrity on consumers' impulsive buying behavior. The independent variables are divided into three dimensions, including personal charm, professionalism and relationship strength. Through the literature collation and research, the maturity scale was used to compile the questionnaire. Analyzing the results of questionnaire survey, we find that personal charm and relationship strength have a positive effect on consumers' impulsive buying behavior. Consumer emotion has a positive effect on consumers' impulsive buying behavior. As an intermediary, consumers' emotions play a role between independent variables and dependent variables. This study expands the theoretical research on web celebrity characteristics and constructs a mediation model, which has certain practical significance for the study of impulsive buying behavior of consumers.
Range-free localization and tracking in wireless sensor networks

Wireless sensor networks (WSN) have been considered as promising tools for many location-dependent applications such as area surveillance, search and rescue, mobile tracking and navigation, etc. In addition, the geographic information of sensor nodes can be critical for improving network management, topology planning, packet routing and security. Although localization plays an important role in all those systems, itself is a challenging problem due to extremely limited resources available at each low-cost sensor node. Previous research generally divides into two groups: range-based and range-free. Range-based methods are accurate but costly for requiring per-node ranging hardware, careful system calibration, or extensive environment profiling. Range-free approaches feature reduced overhead at the resource constrained sensor node side, nevertheless, with less accuracy depending on anchor density, network connectivity, event distribution, etc. This thesis offers novel solutions to bridge the gap between low cost and high accuracy for range-free localization. In the first part, we explore uncontrolled event-driven localization that advances the state-of-the-art an important step towards a usable system. As the first to apply the concept of sequence to localize nodes, our designs significantly improve system flexibility by providing a trade-off between physical cost (anchors) and soft cost (events), a useful layer of abstraction that adopts different sensing modalities, and a potential option of achieving node positioning via natural ambient events. In the second part, we focus on the challenging problem of localization with merely range-free sensing results. Different from binary proximity, we invent the signature distance as a metric that, for the first time, enables quantifying distance relationships among neighboring nodes with sub-hop resolution in a range-free manner. With little overhead, this metric can be conveniently applied for enhanced system accuracy. We further extend the discovery to mobile target tracking. By converting the tracking problem from sequential localization to a maximum likelihood shortest path searching in a graph, we demonstrate robust tracking under unreliable sensing and without complex movement modeling. By investigating into two important branches of range-free localization - event-driven localization, and localization with local sensing - the research presented in this thesis aims at promoting the use of low-cost range-free solutions in real world applications.
How Do Programmers Express High-Level Concepts using Primitive Data Types?

We investigated how programmers express high-level concepts such as path names and coordinates using primitive data types. While relying too much on primitive data types is sometimes criticized as a bad smell, it is still a common practice among programmers. We propose a novel way to accurately identify expressions for certain predefined concepts by examining API calls. We defined twelve conceptual types used in the Java Standard API. We then obtained expressions for each conceptual type from 26 open source projects. Based on the expressions obtained, we trained a decision tree-based classifier. It achieved 83 % F -score for correctly predicting the conceptual type for a given expression. Our result indicates that it is possible to infer a conceptual type from a source code reasonably well once enough examples are given. The obtained classifier can be used for potential bug detection, test case generation and documentation.
Detection of HTTP Attacks using Machine Learning

Distributed denial of service (DDoS) attacks are considered to be one of the most occurred attacks recently. Internet service and cloud providers suffer from various types of DDoS attacks. Such an attack causes the service unreachable or unresponsive to its legitimate clients. HTTP is a type of volumetric DDoS attack in which an adversary sends a massive number of HTTP requests to the victim's webserver, consuming all available resources and making the webserver unavailable to its intended clients. This article proposed a detection model to successfully predict HTTP DDoS attacks. Our proposed model employed a set of machine learning approaches for DDoS attack detection and classification. The CIDDS-001 dataset is used in our experimental results. The evaluation metrics proved that the KNN and random forest algorithms obtained higher degree of accuracy result compared with the other machine learning methods.
Longitudinal Control for Ultra Light Weight Guided Parachute Systems

† Senior Electrical Engineer, AIAA Member ‡ Electrical Engineer, AIAA Member § Research Engineer, AIAA Senior Member ¶ Research Associate Longitudinal Control for Ultra-Light Weight Guided Parachute Systems Keith Bergeron*, Greg Noetscher, Michael Shurtliff, Steven Tavan US Army Natick Soldier Research, Development and Engineering Center, Natick, MA 01760 Frank Deazley Aerial Delivery Solutions LLC, 2601 Keswick Court, Kissimmee, FL 34744
Transactive Energy: Power Electronics Challenges

Transactive Energy (TE) is an evolving concept that combines information and energy in order to enable highly coordinated energetic transactions. Different sources define transactive energy in different ways. The GridWise Architecture Council (GWAC) defines TE as “A system of economic and control mechanisms that allows the dynamic balance of supply and demand across the entire electrical infrastructure using value as a key operational parameter” [1]. This definition is general enough to holistically cover all TE related issues. In a top-down approach, a question asked is if power electronics (PE) is ready to face the challenges posed by TE. Other questions are: What will be the role of PE in the future TE systems? What type of PE devices will play a key role?
An Empirical Study on the Usage and Evolution of Identifier Styles in Practice

Identifiers play an important role in helping developers comprehend and maintain source code. In practice, developers usually employ two widely-used identifier styles, i.e., snake case and camel case, to format identifiers to make them understandable and informative. Despite researchers have empirically investigated the impacts of identifier styles on code comprehension activities, the usage and evolution of identifier styles, however, have not been fully explored. How are individual identifier styles formed in practice? How would identifier styles change and evolve? What are the potential impacts of identifier style-changes? Questions like these are important but have not been fully answered yet. In this paper, we conducted an empirical study on 9,792 GitHub projects to gain some insights into these problems. Specifically, we first analyzed how different identifier styles were formed in real software projects. Next, we explored the change patterns of identifier styles along with the project evolution. Finally, we investigated the potential impacts as well as categories of identifier style-changes. Our empirical results achieved some interesting findings. For example, we first reported some identifier style-change patterns (e.g., snake case →camel case → snake case), which could help developers resolve style-change problems in practice. Our study also provided some hints for researchers and developers when they use specific identifier styles in programs. For example, when researchers explore the impacts of identifier styles on code comprehension, they are suggested to consider the imbalanced distribution phenomenon of individual identifier styles. Besides, it is worthwhile for developers to build an identifier style-change prediction and propagation tool to reduce the style-change costs.
Data Flow Testing for PLC Programs via Dynamic Symbolic Execution

Programmable logic controllers (PLCs) are broadly used in the safety-critical industrial field, which requires high reliability to avoid catastrophes. Data flow testing (DFT) focuses on data flow relationships in a program and has a stronger fault-detection ability than other control flow-based testing. However, there is no automated testing tool supporting DFT for PLC programs. Hence, we propose an automated data flow testing framework for PLC programs. Our DFT framework is based on dynamic symbolic execution (DSE). Considering the cyclic execution feature of PLC programs, our approach needs reachable states which can be provided by branch testing. Besides, our approach improves testing performance through a novel guided path search algorithm. Furthermore, we evaluate our approach on several programs to demonstrate that this approach is practical and effective.
Current Situation and Optimization of the Combined Development of E-Commerce and the Rural Cultural Tourism -- Based on Big Data and Data Visualization Method

By using the data visualization analysis method, this paper makes an in-depth analysis of the current situation of integrated development of E-commerce and rural cultural tourism industry in China, analyzes the effect of e-commerce on the rural cultural tourism industry, and clarifies the factors restricting the development of rural e-commerce tourism in China at this stage. To further promote the development of higher quality of rural e-commerce tourism industry in China, it provides reference suggestions, that is, based on the e-commerce platform, build characteristic brands of rural tourism, provide sufficient transportation, communication, and talent support, and speed up the in-depth integration development of rural tourism industry chain and rural e-commerce. In addition, this paper uses the swot analysis tool, from the strengths, weaknesses, opportunities, threats, such four dimensions, and introduces the advantages and disadvantages of e-commerce in rural cultural tourism in detail. Finally, this paper, through structure design, finds that e-commerce can help the development of rural cultural tourism.
Improving Semantic Consistency of Variable Names with Use-Flow Graph Analysis

Consistency is one of the keys to maintainable source code and hence a successful software project. We propose a novel method of extracting the intent of programmers from source code of a large project (~ 300 kLOC) and checking the semantic consistency of its variable names. Our system learns a project-specific naming convention for variables based on its role solely from source code, and suggest alternatives when it violates its internal consistency. The system can also show the reasoning why a certain variable should be named in a specific way. The system does not rely on any external knowledge. We applied our method to 12 open-source projects and evaluated its results with human reviewers. Our system proposed alternative variable names for 416 out of 1080 (39%) instances that are considered better than ones originally used by the developers. Based on the results, we created patches to correct the inconsistent names and sent them to its developers. Three open-source projects adopted it.
High-Frequency Split-Bobbin Transformer Design with Adjustable Leakage Inductance

While designing a high-frequency transformer, its parasitic components should be modeled with caution as it influences the performance of the transformer. In many converter topologies, a transformer with specific parasitic components could be needed. For example, in the phase-shifted full-bridge converter, bidirectional dual active bridge converter dc-dc converters, or LLC resonant converters, the leakage inductance of the transformer needs to be precise as it influences the overall performance of the converter. In this paper, a simple yet effective technique has been investigated to design high-frequency transformers with adjustable leakage inductance. The transformer is designed to be used in a 100 kHz series resonant dc-dc converter, where the leakage inductance of the transformer is used as the resonant inductor. The proposed design technique utilizes the variation of the winding structures where a split bobbin separates primary and secondary windings. At first, the finite element analysis (FEA) based simulation is performed to estimate and analyze the leakage inductance with different winding structures, and then the experimental investigations are performed. Both the simulation and experimental results demonstrate that by simply varying the winding arrangements in the hybrid split-bobbin transformer, the leakage inductance can be changed while keeping the magnetizing inductance of the transformer constant.
Parameter Optimization of Spark in Heterogeneous Environment Based on Hyperband

There are many default parameters in Spark, which have a great influence on job performance. Different jobs require different parameter combos, resulting in the need to tune parameters with experience which causes low efficiency. To improve the performance of Spark in the heterogeneous environment, the Hyperband algorithm is used to optimize the Spark parameters to improve the efficiency of the Spark platform. To emulate the actual production environment and improve operation efficiency as possible, this study uses Spark on yarn mode to test. Experiments are carried out on WordCount and Sort job types. The results show that, taking 5GB dataset as an example, compared with the default parameters. , this method reduces the job execution time by 12.94% and 10.41%, respectively.
AUDIT AWAL SISTEM INFORMASI PADA PT. X BERDASARKAN STANDAR CONTROL OBJECTIVES FOR INFORMATION AND RELATED TECHNOLOGY (COBIT 4.1)

PT. X is a company engaged in the field of iron and steel construction, located in Surabaya. Not easy for a large company with business processes that are also wide enough to carry out all processes in the company by way of the manual. This is because in the era of globalization, companies must be able to address the issues and changes that occur quickly and on target. Therefore, companies urgently need an integrated application. However, the factor that must be considered not only focuses on the acquisition of application systems needed by the company alone. But companies also need to focus on maintaining the quality of the application system. Because the application system useful to support and enhance the company's management in order to improve company performance and effectiveness in delivering services to the public. Therefore, the researcher wants to conduct the audit planning of application system for PT. X with analysis on the condition of IT at PT. X to determine the COBIT domains that need to be audited.
Diabetes, heart damage, and angiotensin II. What is the relationship link between them? A minireview

Abstract Cardiovascular complications are the main cause of mortality and morbidity in the diabetic patients, in whom changes in myocardial structure and function have been described. Numerous molecular mechanisms have been proposed that could contribute to the development of a cardiac damage. In this regard, angiotensin II (Ang II), a proinflammatory peptide that constitutes the main effector of the renin-angiotensin system (RAS) has taken a relevant role. The aim of this review was to analyze the role of Ang II in the different biochemical pathways that could be involved in the development of cardiovascular damage during diabetes. We performed an exhaustive review in the main databases, using the following terms: angiotensin II, cardiovascular damage, renin angiotensin system, inflammation, and diabetes mellitus. Classically, the RAS has been defined as a complex system of enzymes, receptors, and peptides that help control the blood pressure and the fluid homeostasis. However, in recent years, this concept has undergone substantial changes. Although this system has been known for decades, recent discoveries in cellular and molecular biology, as well as cardiovascular physiology, have introduced a better understanding of its function and relationship to the development of the diabetic cardiomyopathy.
Exercise training improves memory and produces changes in the adrenal gland morphology in the experimental autoimmune encephalomyelitis

Abstract Objective. The present study sought to verify the effects of an exercise training on the memory along with the morphological assessment of the adrenal gland tissue in the rats with experimental autoimmune encephalomyelitis (EAE). Methods. Female Lewis rats were randomly divided into three groups: EAE group, EAE group with exercise (EAE+Ex), and control group (CO). Each group contained 10 rats. To evaluate the memory, all rats were subjected to the Morris water maze learning test for four consecutive days and one day for a prop test. EAE was induced by guinea pig spinal cord homogenate emulsified in incomplete Freund’s adjuvant and heat-mycobacterium. The exercise training on a motorized treadmill was initiated 3 weeks before EAE induction and disconnected 2 weeks post-induction. Results. We found that exercise training for five weeks produced an improved swimming velocity related to memory improvement in EAE+Ex group in comparison with EAE group, but not an incurable adrenal gland tissue after EAE induction. Conclusions. The experimental design selected for this study appears to be an effective treatment for memory in rats with experimental autoimmune encephalomyelitis.
Tree-based Mining of Fine-grained Code Changes to Detect Unknown Change Patterns

In software development, source code is repeatedly changed due to various reasons. Similar code changes are called change patterns. Identifying change patterns is useful to support software development in a variety of ways. For example, change patterns can be used to collect ingredients for code completion or automated program repair. Many research studies have proposed various techniques that detect change patterns. For example, Negara et al. proposed a technique that derives change patterns from the edit scripts. Negara's technique can detect fine-grained change patterns, but we consider that there is room to improve their technique. We found that Negara's technique occasionally generates change patterns from structurally-different changes, and we also uncovered that the reason why such change patterns are generated is that their technique performs text comparisons in matching changes. In this study, we propose a new change mining technique to detect change patterns only from structurally-identical changes by taking into account the structure of the abstract syntax trees. We implemented the proposed technique as a tool, TC2P, and we compared it with Negara's technique. As a result, we confirmed that TC2P was not only able to detect change patterns more adequately than the prior technique but also to detect change patterns that were not detected by the prior technique.
Analysis of Spam Detection Using Integration of Logistic Regression and PSO Algorithm

Content-based text classification system can automatically categories the text document into predefined limited classes. But the e-mail document classification is a challenging process in the modern internet environment. The e-mail documents are lightly signified in a great dimensional features space, creating a learning process, and the generalization (abstraction) process is problematic. Spam is unsolicited mail, the bulk of spam mails sent by the spammers who use vast e-mail programs to cover their characteristics and send the spam mails every day with no money. The spam mail directs various effects, including exposing unwanted images, decreasing company productivity, blocking Internet Service Providers' (ISP) networks, etc. Additionally, the spam mail contains an infection, which is gotten ready for some fake movement. Newline PSO is mighty in taking care of multivariable issues, where factors take on genuine qualities that are taken as new lines an independent method to arrange the datasets and contemplate the PSO-based Classifier for Multiclass Data Sets. Spam has become the foundation of decisions utilized by digital crooks to spread vindictive payloads, for example, infections and Trojans. Community-oriented spam recognition methods can manage the enormous scope of electronic communication information subsidized through numerous causes, and special issues needing exposure to electronic communication information. Distance-protecting hash standard arrangements utilized to save the security of electronic communication information while empowering mails orders to detect spam recognition. PSO, a Big Data security safeguarding synergistic spam identification stage based on top of a standard Map Reduce office. Without PSO feature selection, the training accuracy would be less. With the help of the best dataset fit, the classification result will be high.