Reviewerly/exHarmony huggingface dataset:
United We Stand, Divided We Fall: Leveraging Ensembles of Recommenders to Compete with Budget Constrained Resources

In this paper we provide an overview of the approach we used as team Surricchi1 for the ACM RecSys Challenge 20221. The competition, sponsored and organized by Dressipi, involves a typical session-based recommendation task in the fashion industry domain. Our proposed method2 leverages an ensemble of multiple recommenders selected to capture diverse facets of the input data. Such a modular approach allowed our team to achieve competitive results with a score of 0.1994 Mean Reciprocal Rank at 100 (∼ 7.6% less than the first qualified team). We obtained this result by leveraging only publicly and freely available computational resources 3 and our own laptops. Part of the merit also lies in the size of this year’s dataset (∼ 5 million data points), which democratized the challenge to a larger public and allowed us to join the challenge as independent researchers.
Analisis Faktor Pendukung dan Penghambat Pembelajaran Online Saat Pandemi diMA Midanutta'lim Mayangan Jogoroto

The purpose of this study is to find out the supporting and inhibiting factors for online learning during the pandemic at MA Midanutta'lim Mayangan Jogoroto. This research is a qualitative study with descriptive analysis techniques. The results of this study are the COVID-19 pandemic has had a very large impact on the learning process, learning that is usually carried out directly is now being shifted to online learning by planning and implementing, the planning is to make materials and social evaluations,while the implementation is to distribute materials and assignments. to the WhatsApp group. Students feel bored and bored while carrying out learning. Online learning conducted for elementary school age children is considered less effective. There are several factors that support teachers in the online learning process, namely the availability of mobile phones, quotas and a stable internet network. In addition to the factors that support online learning, there are also several inhibiting factors for teachers in online learning. The inhibiting factors include not all students have cellphones and there are still many parents who are busy working.
The who, what and how of the current research at the Brazilian Symposium on Software Engineering

Software engineering is a socio-technical activity, and while many contributions focus on technical aspects, its research should benefit human stakeholders. The Who-What-How framework aims to capture the head beneficiary of a research study (the who), the predominant type of research contribution produced (the what), and the research strategies used in a study (the how), evaluating how these studies address the human aspects. This paper performs a replication study of “The who, what, how of software engineering research: a socio-technical framework” [50], which addressed the question of how papers from the ICSE and EMSE considered human aspects. This study uses the Who-What-How framework to analyze works published at the Brazilian Symposium on Software Engineering between 2019 and 2021. We analyzed 90 papers and found a significant amount of work that claims to benefit human stakeholders, most presented descriptive contributions and adopted the Respondent research strategy. Triangulation of research was low, as only 15 papers reported using mixed strategies.
Functional Suitability Assessment:

The demand for software that is executed in different contexts and by different users brings to light the importance of evaluating the quality attribute “functional suitability”, since this software attribute deals with the completeness of functionalities and the correctness of their executions, always appropriate to specific contexts. Despite recommendations on the subject, ISO SQuaRE 25000 recommends what needs to be evaluated when functional suitability is observed. The abstraction inherent in this process may present itself as an obstacle in adhering to these standards. Therefore, this paper presents the results of an opinion survey conducted through interviews with software companies in order to understand the perceptions and trends regarding the evaluation of functional suitability in the software industry. The results, although initial and not generalizable, supported the discussions on the quality assessment process, as well as the tools and metrics adopted, in addition to the stakeholders involved. It was observed in a preliminary way that the concept of this attribute is sometimes unknown, and sometimes ignored, given the complexity and the cost of performing tests to measure it. Completeness and correctness are the most analyzed characteristics, to the detriment of the functional appropriateness, which is measured indirectly through the acceptance of the delivered functionalities.
Understanding Thresholds of Software Features for Defect Prediction

Software defect prediction is a subject of study involving the interplay of the software engineering and machine learning areas. The current literature proposed numerous machine learning models to predict software defects from software data, such as commits and code metrics. However, existing machine learning models are more valuable when we can understand the prediction. Otherwise, software developers cannot reason why a machine learning model made such predictions, generating many questions about the model’s applicability in software projects. As explainable machine learning models for the defect prediction problem remain a recent research topic, it leaves room for exploration. In this paper, we propose a preliminary analysis of an extensive dataset to predict software defects. The dataset includes 47,618 classes from 53 open-source projects and covers 66 software features related to numerous features of the code. Therefore, we offer contributions on explaining how each selected software feature favors the prediction of software defects in Java projects. Our initial results suggest that developers should keep the values of some specific software features small to avoid software defects. We hope our approach can guide more discussions about explainable machine learning for defect prediction and its impact on software development.
Towards a Process to Manage Usability Technical Debts

Background: Technical Debt (TD) is a metaphor used to refer to the consequences of poor software development. TD can arise from immature source code to quality requirements that are not adequately addressed. Usability issues can be identified during a software project and, in turn, treated as TD. Studies conducted on real industry projects indicated that Usability Technical Debt (UTD) is frequent in software projects, is associated with relevant usability issues, and usually requires low effort for their payment. The systematic identification and management of UTD can contribute to how the project team acts in the face of usability problems, preventing these types of problems from reaching end-users and generating a positive impact on the use of the software. Aim: This research aims to provide a process that supports the early identification and management of UTD. Method: We adopted Design Science Research to create a process that comprises the identification, validation, rating, prioritization, estimation, and monitoring of UTD. Results: This paper presents the process with proposed activities, actors, and artifacts. Conclusions: We expect the process supports software development teams to deal with UTD systematically and contributes to improving the quality of the products developed.
Investigating the Relationship between Software Team Leadership Styles and Turnover Intention

In other fields than Software Engineering, previous studies observed that leadership styles could influence employee turnover. This turnover is prominent, especially among software developers, who have identified personal and work issues related to this career decision. This paper is an initial and exploratpry study that investigates the relationship between turnover intention and leadership styles in software teams. These leadership styles were analyzed according to the Full Range Leadership Model (FRLM) using the 6S version of the Multifactor Leadership Questionnaire. We applied it to Brazilian developers, in which we obtained 104 responses. Initial results indicate that developers perceive no significant difference between transformational, transactional, and passive/avoidant leaders, suggesting that these leaders may exhibit mixed styles and characteristics. No leadership style index is significantly correlated with turnover intention, suggesting that these indices are less critical to turnover than other variables previously studied in the literature. Furthermore, the results show that transformational and transactional styles are positively correlated, indicating that these styles can develop together, promoting motivation, guidance, and contributing leaders for self-managed teams, where they can use the passive/avoidant leadership style to determine effective leadership in agile teams. These results were the starting points for our current investigation into leadership in software teams, which aims to observe how effective leadership occurs within teams and how non-leaders perceive this leadership, helping management understand leadership characteristics and how they potentially interfere with development activities.
(Retracted) Method of defogging unmanned aerial vehicle images based on intelligent manufacturing

Intelligent manufacturing is a major trend in manufacturing innovation around the world, and it is also the main direction and key breakthrough point for the transformation and upgrading of the manufacturing industry for a long time now and in the future. Here, we mainly study the role of intelligent manufacturing in defogging unmanned aerial vehicle (UAV) images and how to analyze UAV image defogging methods based on intelligent manufacturing. In recent years, with the continuous progress of UAV technology, UAV project has also been booming. Aerial photographing is one of the most widely used functions of UAV at present. However, when photographing images in severe weather environment such as haze, it will be affected by the absorption and scattering of light by a variety of different suspended substances in the environment, resulting in poor imaging quality, color distortion, fine pitch blur, and other adverse effects. For this reason, there is a large amount of research on the image haze removal of UAV in China. At present, the mainstream methods are based on non-vision sensor and physical model. However, due to the lack of haze removal effect and severe condition limitation in the practical application of the above methods, we explore an innovative set of automatic estimation haze removal method of atmospheric light based on this method, taking the principle of atmospheric estimation direction as the main research direction, and then through the steps of setting the global transmittance, calculating the atmospheric light amplitude, adjusting the image size, and automatically modifying the image condition threshold value, etc. The man–machine image is haze removal. To compare the advantages and disadvantages of the three methods, we choose standard deviation, information entropy, and objective evaluation method to analyze the results of the three methods. Data analysis shows that among the three haze removal methods, the atmospheric light automatic estimation haze removal method has greatly improved the overall haze removal effect. Compared with the previous two kinds of haze removal imaging in the haze environment, it can better guarantee the color balance degree, and also retain more image information, which makes the imaging clarity have a significant improvement, and the overall effect is more natural. This method has played a very good complementary role to the domestic UAV demisting technology.
A Typed Lambda Calculus with Gradual Intersection Types

Intersection types have the power to type expressions which are all of many different types. Gradual types combine type checking at both compile-time and run-time. Here we combine these two approaches in a new typed calculus that harness both of their strengths. We incorporate these two contributions in a single typed calculus and define an operational semantics with type cast annotations. We also prove several crucial properties of the type system, namely that types are preserved during compilation and evaluation, and that the refined criteria for gradual typing holds.
Web Mining Techniques Applicable for Cultural Heritage Observations

The rapid digitalization in the cultural domain generated enormous amounts of data. But that data is not personalized or processed, thus the benefit for different users is limited, due to the variety of users – people learn through different methods. However if techniques used in the last decade to personalize web content are applied to cultural content more users could easily absorb and process the cultural heritage knowledge. Also techniques could be applied to cluster and extract useful knowledge.
Opportunities and Future Directions of Human-Metaverse Interaction

In the COVID-19 pandemic era, non-contact services were demanded and the use of extended reality and metaverse services increased rapidly in various applications. In this paper, we analyze Gather.town, ifland, Roblox, and ZEPETO metaverse platforms in terms of user interaction, avatar-based interaction, and virtual world authoring. Especially, we distinguish interactions among user input techniques that occur in the real world, avatar representation techniques to represent users in the virtual world, and interaction types that create a virtual world through user participation. Based on this work, we highlight the current trends and needs of human-metaverse interaction and forecast future opportunities and research directions.
More Effects and Digital Audio Workstations

This chapter will feature six of the best digital audio workstations (DAWs) for video game audio design. We will look at their capabilities and basic features, covering software for modern Windows, macOS, and Linux operating systems. Specifics for voice acting will be introduced in Chapter 5 and for composition in Chapter 6 . But first, we will explore plugins and some time-based effects, many of which have been recording studio staples (and curses) since the beginning.
Blockchain-Enabled Traceability in Sustainable Food Supply Chains: A Case Study of the Pork Industry in Vietnam

Blockchain-enabled traceability has been widely touted as having a great potential for improving food supply chains. Proposed benefits include: outbreak tracing, verifying ingredient origins, monitoring environmental compliance and automating payments. Blockchain-enabled food supply chains have not been widely studied in developing countries. Therefore, a comprehensive evaluation of blockchain-enabled traceability in food supply chains was conducted to gain a deeper understanding of the potential impacts on food supply chain sustainability. A case study of a pork supply chain in Vietnam was analysed to understand the context in a developing country. The analysis was conducted through the lens of a triple-bottom-line blockchain framework for supply chains.
Construction of Evaluation System of Application Effect of Electronic Schoolbag in University Classroom Teaching

To evaluate the effect of introducing E-Book Package into college classroom teaching and construct an evaluation index system, delphi expert investigation method, AHP analytic hierarchy process and fuzzy evaluation method were used in this paper. Firstly, through literature analysis, the evaluation indexes for evaluating the effect of introducing E-Book Package into university classroom teaching are summarized, and then the weight system of these indexes is determined by AHP and Delphi expert investigation method. Next, the evaluated objects are scored by fuzzy evaluation method. Through the scoring and evaluation process of the evaluation system, we can give specific suggestions for the evaluated object to improve the effect of introducing E-Book Package into college classroom teaching.
Research on the Application of BP Neural Network in the Evaluation of College Teaching Quality

With the expansion of colleges and universities, the society and public are more and more concerned about the quality of students' learning. For colleges and universities, how to improve the teaching quality of teachers is a problem that needs to be considered. Because of the diversity of teaching and students and the supervisory factors of the educational affairs department, it is difficult to be objective and fair in teaching quality evaluation. In order to improve the accuracy of the current college teaching quality evaluation, this paper applies the BP neural network to the college teaching quality evaluation. This paper constructs a BP (Back Propagation) neural network model for college teaching quality evaluation and verifies the accuracy of the BP neural network through experiments. The results show that the BP neural network established in this paper can evaluate the college teaching quality well.
Mobile Application for the Recommendation of Furniture and Appliances Through Augmented Reality to Improve The User Experience in The Online Shopping Process

Due to the increase in online shopping, companies are looking to reinvent themselves and improve their ecommerce platforms. However, some users still do not dare to make an online purchase mainly because they cannot physically view the product. As a result of this, innovative technologies have started to emerge. Therefore, this project seeks to improve the customer's user experience, using augmented reality and colour recognition to recommend products in a mobile application. For the development of the application, ARCore was used as a software development kit (SDK) to display products and take measures with augmented reality. In addition, a colour recommendation was implemented using the Palette API (Application Programming Interface) for colour recognition and an algorithm based on colour theory to generate the combinations. The usability of the application was evaluated by a group of users, who completed a form that was based on Nielsen's heuristic principles. The main conclusions of the analysis of the responses are that they would make purchases using the application without having to physically see the product, users understood the purpose of using augmented reality and colour recognition to obtain recommendations. In addition, they were able to use all the functionalities implemented in the application without the need for a user manual, which highlights the minimalist design and ease of use of the application.
The Influence of Parameter Differences of Virtual Reality Device on Student Experience

In recent years, virtual reality (VR) device has been widely used in education. The visual presentation, sound effect, tactile feedback, etc., of virtual reality device greatly enhance the immersion and interactivity of student experience. Although VR devices are constantly updated, related problems still exist, such as bulky size, poor comfort, and low capability. At present, there are few research on educational applications using VR devices. This paper takes the educational application using head-mounted virtual reality display devices as the main research direction and proposes specific evaluation criteria for the satisfaction of student experiences. The influence of the parameter difference of the head-mounted VR device on student experience is determined by selecting students and teaching cases, matching teaching and learning methods, designing teaching and learning processes, collecting and analyzing student experience data, etc. Moreover, we provide advice on parameter selection of future terminal devices.
Technical Background

This chapter provides the technical background of the collaborative fleet maneuvering problem. The basic knowledge of vehicle model and fleet configuration is introduced. Main approaches on topics related to fleet maneuvering, including collaborative localization, fleet keeping and reconstruction, and collision avoidance, are reviewed.
SKLEE: A Dynamic Symbolic Analysis Tool for Ethereum Smart Contracts (Tool Paper)

We present SKLEE, a dynamic symbolic framework to analyse Solidity smart contracts for various safety vulnerabilities. While there are many analysis tools for Solidity contracts, in this work we demonstrate that existing analysis infrastructures for other sequential programming languages, such as C, can be leveraged to construct a competitive analysis framework for Solidity contracts. Notably, SKLEE is bootstrapped on top of KLEE – a dynamic symbolic test-case generation tool for C programs – with modelling for Solidity primitives such as send, call, transfer, and others. Our experiments indicate that SKLEE is indeed competitive with other state-of-the-art tools in terms of (i) the number of bug classes it can identify, and (ii) the number of benchmarks it can analyse in a given time bound.
Chiptunes

Having only briefly mentioned the concept of chiptunes previously in the book, it's time we fully explore this rather timeless genre of music. We'll be using numerous concepts familiar from previous chapters while introducing many more. Let's begin with the definitions of the three eras of video game music. The rest of the chapter will deal with some of the best software for creating chiptunes with.
Bits, Sample Rates, and Other Fundamentals of Digital Audio

We'll start off by exploring the very fundamentals of digital audio. This chapter covers topics like bit rates, pulse-code modulation, and common digital audio formats. Having a good grasp of said topics is essential when getting serious about your video game audio work. We won't be going into detail as to how to implement this know-how just yet. That is saved for later chapters.
Implications in the Evaluation of Costs in Distance and Face-to-Face Education through the “Break-even-point” Technique, in Bachelor's Degree in Sociology

Universities, both public and private, seek to implement a university policy that tries to address the academic, administrative, educational and economic problems would be faced with the concrete fact that ignore the behavior of those categories that explain such drawbacks. In the best case, too specific and traditional elements and equipment shortages are known problems, lack of training of certain administrative levels, inadequate buildings, the development of modules, presentation advice and obviously a relationship is established directly between the magnitude of these problems and inadequate financial resources. This project made the application of the technique "Break-even-point" (Breakeven) Costs of Distance Education and Higher Education Education Classroom. The project presents the ratio of fixed costs and variable costs and the influence of the number of students in the Semester Program Total Cost per case BA in Sociology.
Distributed inference for multiple DNN models in IoT environments

Edge Computing (EC) has been promising in providing support for Deep Neural Network (DNN) applications in IoT environments. However, resources in each edge are limited as response time increases. To reduce the latency, we propose to distribute the computation of multiple DNN models to nearby IoT devices. In particular, we propose a piece-wise multilevel partitioning and scheduling algorithm to improve the completion time of DNN inference.
Partial federated learning based network intrusion system for mobile devices

We propose a partial federated learning based network intrusion system to utilize the limited communication resources of mobile devices. The key to our algorithm is that we share only part of the model to enable efficient communication and strengthen data privacy. The proposed method is evaluated using two network traffic datasets and three performance measures. Our simulation results confirm that the model can reach the performance of centralized learning, although the ratio of a shared layer is reduced up to 25%.