Reviewerly/exHarmony huggingface dataset:
Nonlinear Deblurring for Low-Light Saturated Image

Single image deblurring has achieved significant progress for natural daytime images. Saturation is a common phenomenon in blurry images, due to the low light conditions and long exposure times. However, conventional linear deblurring methods usually deal with natural blurry images well but result in severe ringing artifacts when recovering low-light saturated blurry images. To solve this problem, we formulate the saturation deblurring problem as a nonlinear model, in which all the saturated and unsaturated pixels are modeled adaptively. Specifically, we additionally introduce a nonlinear function to the convolution operator to accommodate the procedure of the saturation in the presence of the blurring. The proposed method has two advantages over previous methods. On the one hand, the proposed method achieves the same high quality of restoring the natural image as seen in conventional deblurring methods, while also reducing the estimation errors in saturated areas and suppressing ringing artifacts. On the other hand, compared with the recent saturated-based deblurring methods, the proposed method captures the formation of unsaturated and saturated degradations straightforwardly rather than with cumbersome and error-prone detection steps. Note that, this nonlinear degradation model can be naturally formulated into a maximum-a posterioriframework, and can be efficiently decoupled into several solvable sub-problems via the alternating direction method of multipliers (ADMM). Experimental results on both synthetic and real-world images demonstrate that the proposed deblurring algorithm outperforms the state-of-the-art low-light saturation-based deblurring methods.
Short-Term Precipitation Forecasting Based on the Improved Extreme Learning Machine Technique

In this study, an improved version of the Extreme Learning Machine, namely the Improved Weighted Regularization ELM (IWRELM), is proposed for hourly precipitation forecasting that is multi-steps ahead. After finding the optimal values of the proposed method, including the number of hidden neurons, the activation function, the weight function, the regularization parameter, and the effect of orthogonality, the IWRELM model was calibrated and validated. Thereafter, the calibrated IWRELM model was used to estimate precipitation up to ten hours ahead. The results indicated that the proposed IWRELM (R = 0.9996; NSE = 0.9993; RMSE = 0.015; MAE = 0.0005) has acceptable accuracy in short-term hourly precipitation forecasting up to ten hours ahead.
Multi-granularity user interest modeling and interest drift detection

Since the advent of Web 2.0 culture, there as been an explosion of data on the internet. The traditional service model based on the search engine can no longer meet the increasing demand for personalized service. Taking the Douban film review platform as an example in this paper, we propose a method to model user preferences and detect preference drift. Based on a hierarchical topic tree and tilted time window, we design a hierarchical classification tree, named HAT-tree, to maintain the history of the user’s preferences at multi-topic and multi-time granularity. We identify the user’s primary historical preferences, predict their future primary preferences and also detect user preference drift. The proposed algorithm can find the user’s long-term and short-term preferences, detect the user’s explicit and implicit preference drift, and highlight the importance of the user’s more recent preferences. Many experiments are carried out on multiple data sets, and the experimental results show that the proposed method is more accurate than other similar algorithms of user preference drift detection.
Intrusion detection algorithom based on transfer extreme learning machine

Intrusion detection can effectively detect malicious attacks in computer networks, which has always been a research hotspot in field of network security. At present, most of the existing intrusion detection methods are based on traditional machine learning algorithms. These methods need enough available intrusion detection training samples, training and test data meet the assumption of independent and identically distributed, at the same time have the disadvantages of low detection accuracy for small samples and new emerging attacks, slow speed of establishment model and high cost. To solve the above problems, this paper proposes an intrusion detection algorithm-TrELM based on transfer learning and extreme machine. TrELM is no longer limited by the assumptions of traditional machine learning. TrELM utilizes the idea of transfer learning to transfer a large number of historical intrusion detection samples related to target domain to target domain with a small number of intrusion detection samples. With the existing historical knowledge, quickly build a high-quality target learning model to effectively improve the detection effect and efficiency of small samples and new emerging intrusion detection behaviors. Experiments are carried out on NSL-KDD, KDD99 and ISCX2012 data sets. The experimental results show that the algorithm can improve the detection accuracy, especially for unknown and small samples.
Study on the Optimal Reaction Parameters of C4 Olefin Prepared by Ethanol Catalyzed Reaction Based on XGBoost Regression Prediction Algorithm

Abstract The production of C4 olefin by ethanol coupling catalytic reaction has a broad development prospect in the field of renewable energy. In this paper, the influencing factors of C4 olefin preparation process were studied, the influence of temperature and catalyst type on the reaction was analyzed, and the multivariate nonlinear regression fitting model was established under the assumption of ignoring the small probability of experimental error. Then, based on the grey correlation analysis model, the correlation degree between each variable and ethanol conversion rate and C4 olefin selection rate was studied. Then, using the catalyst parameter variables considered above, the relationship equations between four catalyst parameter variables, temperature and C4 olefin yield were calculated, and the overall constructor model of C4 olefin yield under the joint action of five variables was established by using the constructor method. Finally, using MPai data science platform software based on XGBoost regression prediction algorithm, the established constructor was cross-validated by 50% fold, and the optimal solution of reaction parameters was finally obtained. Finally, five experiments were selected to improve the experimental control group data and verify the accuracy of the theoretical value of the optimal solution. By changing the catalyst parameters and temperature, a relative control group was added to the experimental group, and the conclusion of the theoretical parameter optimal solution under unrestricted and temperature restrictions was experimentally verified.
EnoLight Ltd: a classic entrepreneurship challenge

Research methodology One of the case writers worked as a student in the summer of 2018 in EnoLight, which provided the inspiration for the case. The first author is not tied to the company and provides an unbiased perspective. The information presented in the case and the quotes were sourced from an interview with Farzad Moghiman in the Fall of 2018, email and phone follow-up with him, and information the second author remembered from his time at EnoLight, which was approved later by Farzad. Supplementary information was obtained from online sources, as cited in the case. Case overview/synopsis Farzad Moghiman, president of EnoLight, has a vision to revolutionize the use of light and bring it to the forefront of artistic designs. The company was founded in late 2016. Over a year was spent developing the business plan, finding additional partners, establishing the company as a limited partnership, finding and negotiating with suppliers and beta-testing its products. It is now time for Farzad to start selling as his funds, which were his lifetime savings, is running out. He knew the first decision to make was the target market. Identifying the first segment to target would help him select the distribution channel and other marketing plan elements. Complexity academic level The main objective of this case is to segment the market and identify the most attractive segment to target. This case offers an opportunity for students to segment both consumer and business markets and experience the significant impact of selecting the target market on other marketing mix elements. Furthermore, students are exposed to the difficulties of a start-up environment, resource constraints and a lack of market credibility – bearing these factors in mind while generating realistic alternatives. This case can be used in an introductory marketing course. Learning objectives 1. Apply segmentation variables to segment both business and consumer markets and understand how segmentation and targeting impact other marketing decisions.2. Evaluate different customer segments to select the target market.3. Develop the best positioning strategy for a new startup company.4. Recommend an segmentation, targeting and positioning (STP) plan that meets the company’s financial objective.
Leadership and Innovativeness as the Basis for Developing Teachers’ Digital Competences

The authors discuss the prospects for developing modern teacher’s competences in the process of digitalization of education and several related problems. The purpose of the study is, based on the analysis of scientific research conducted in Russia and some other countries, to provide pedagogical management with a promising system for developing digital competencies in order to enable teachers to master modern educational technologies. Teachers’ innovativeness and leadership as psychological characteristics that help them become digitally competent are considered from a theoretical standpoint. Based on these characteristics, as they are manifested in the educational process, a theoretical model of the structural elements of personal innovativeness is demonstrated. The practical technology for organizing a teacher’s psychological and pedagogical laboratory in the form of a cycle of logically interconnected modules is described in detail. This technology will make it possible to develop teachers’ digital competences in the system of a modern educational institution. The formulated main directions of the system for developing teachers’ innovativeness and leadership can be used in the practice of pedagogical management of any modern school. The authors substantiate the need to introduce this system into the international practice of modern education.
Exploiting spatial and temporal context for online tracking with improved transformer

At present, the transformer is becoming more and more popular in computer vision tasks due to its ability to capture long-range dependencies via self-attention. In this paper, we propose a transformer-based classification regression network TrCAR utilizing the transformer to exploit deeper spatial and temporal context. Different from the classic architecture of the transformer, we introduce convolution operation into the transformer and change the calculation of features to make it suitable for the tracking task. After that, the improved transformer encoder is introduced into the regression branch of TrCAR and combined with the feature pyramid to complete multi-layer feature fusion, which is conducive to obtaining a high-quality target representation. To further enable the target model to adapt to the change of the target appearance, we bring the gradient descent to the regression branch so that it can be updated online to produce a more precise bounding box. Meanwhile, the new transformer is integrated into the classification branch of TrCAR, which as much as possible extracts the essential feature of the target across historical frames via the global computing capability, and uses it to emphasize the target position of the current frame via cross-attention. Which helps the classifier to more easily identify the correct target. Experimental results on OTB, LaSOT, VOT2018, NFS, GOT-10k, and TrackingNet benchmarks show that our TrCAR achieves comparable performance to the popular trackers.
Blockchain in the Metaverse

This chapter seeks to provide a thorough investigation of blockchain for the metaverse in order to better understand its function in the metaverse. An introduction to blockchain and the metaverse is given first, along with a focus on the reasons for using it for the metaverse. There is discussion of the layers of blockchain and the metaverse. The next section discusses technological aspects of blockchain-based approaches for the metaverse, including data collection, storage, sharing, interoperability, and privacy protection. We outline the technological difficulties of the metaverse for each perspective before highlighting how blockchain can be useful. Additionally, we look into how blockchain will affect important metaverse supporting technologies including the Internet of Things, digital twins, multi-sensory and immersive applications, artificial intelligence, and big data. Finally, some prospective avenues for advancing research, innovation, and development in the direction of blockchain's potential usage in the metaverse are provided.
Evaluation of SEU impact on convolutional neural networks based on BRAM and CRAM in FPGAs

This study evaluates the sensitivity of FPGA-based CNN systems and the efficacy of selective hardening approaches with laser injection and proton irradiation. The LeNet-5 CNN architecture was taken as a case study. First, the entire CNN with block random access memory (BRAM) attached is evaluated by laser test scanning, and errors generated from each layer are detected to determine the most critical layer. Second, the same network is evaluated without BRAM, considering only the configuration RAM (CRAM) and errors generated from each layer are detected. Third, the impact of laser scan errors from BRAM and CRAM are compared, while tracing the layers in which these errors are generated. Then, proton testing is performed on the entire CNN, and errors from each layer are detected and compared to validate the laser scanning test. Experimental results from laser and proton demonstrate that CRAM errors have a larger impact on CNN layers than BRAM errors. Regarding layer criticality, the second convolutional layer (C2) appears to be the most critical one because it generates most CRAM errors. This was validated by serial and parallel designs, where both confirm similar trends. Accordingly, the partial triple modular redundancy (TMR) approach is only applied to the critical portions, leading to around 40 % reliability improvement with less than 20 % overhead.
Group Cooperative Teaching Design With Knowledge Graphs in Project-Driven Learning

Using the educational knowledge graph to express the logical characteristics of knowledge, this paper takes project-driven learning in the teaching of “Information Technology in Secondary Schools” as an example and studies the group cooperative teaching mode based on the educational knowledge graph. The relationship between knowledge points in subject courses is described in the form of a knowledge graph, and the learning activities of coordinating the cooperation of each group are completed through the integration process of the knowledge graph. The use of a group cooperation teaching mode in the classroom can not only build a collaborative knowledge system between teachers and students but also help to carry out project-driven learning. The application of the knowledge graph of “Information Technology in Middle School” in the classroom can better show the logic between knowledge, and its graphical structure can help students to structure the knowledge they have learned.
Challenges in Project Initiation

There are various challenges which come in the way of successful project management.The success of any project is defined by how all challenges faced are dealt with.The challenges can even turn into opportunities with proper management and planning.Project management is the implementation of specific knowledge, skills, tools, and procedures to achieve the desired project goals.There are five stages for the successful management of the project.This paper presents the major challenges faced in the project management's step one-Project Initiation.The project initiation phase broadly defines the project goals.Organization comes up with many ideas through brainstorming and then through proper planning ideas are turned into concrete goals for which the whole plan is built up.Once the goal is set, stake holders are defined.There are many challenges faced even at the first stage.The few challenges are thoroughly explained in the paper.The major challenges faced during the project initiation stage are assignment of authority, list of task assignment, project cost planning, prioritizing goals and stakeholders list and their expectations.All are to be properly dealt with, else the success of the project is at risk.Few research papers are critically reviewed to support the arguments stated.One talks about the "Critical Issues Related to Metro Rail Projects in India '' which specifically talks about the metro rail corridor in Delhi.It evaluated the challenges faced by the project manager during the initiation phase of the metro rail project in Delhi.The other is about the "Automobile Manufacturing Industry" where the automobile manufacturing industry in India is discussed.It covers the various challenges faced during the setup of a manufacturing plant.These both papers also illustrate the solutions to the problems faced.
Impact of Mobile Technology and Use of Big Data in Physics Education During Coronavirus Lockdown

The speed of spread of Coronavirus Disease 2019 led to global lockdowns and disruptions in the academic sector.The study examined the impact of mobile technology on physics education during lockdowns.Data were collected through an online survey and later evaluated using regression tools, frequency, and an analysis of variance (ANOVA).The findings revealed that the usage of mobile technology had statistically significant effects on physics instructors' and students' academics during the coronavirus lockdown.Most of the participants admitted that the use of mobile technologies such as smartphones, laptops, PDAs, Zoom, mobile apps, etc. were very useful and helpful for continued education amid the pandemic restrictions.Online teaching is very effective during lock-down with smartphones and laptops on different platforms.The paper brings the limelight to the growing power of mobile technology solutions in physics education.
MDA4SOA : A new model driven architecture to supporting decision making in SOA

The utilization of Model-Driven Architecture (MDA) in Service Oriented Architecture (SOA) is a concept that has been introduced previously and published research is available on this subject. However, the main research s’ubject of this paper, fusing MDA and decision aspects as autonomous building blocks that form a full SOA, is a ground-breaking concept. This paper proposes an integrated model-driven solution method that will help develop service-oriented architectures while being aware of the need for decisions. Furthermore, our goal is to explore the standard models at every defined level; this is one of our primary reasons for utilizing the Service oriented architecture Modeling Language (SoaML) and the Decisional Model Notation (DMN) standard in designing several services. We also present a service taxonomy that defines the new set of concepts needed to model three dimensions. The proposed approach is called MDA4SOA+d (Model Driven Architecture for SOA with Decision Aspect), and it offers, among other benefits, a more comprehensive understanding of the entire system and the platform-independent development. It also enhances the reusability of the designs and simplifies the system’s evolution, thus enhancing productivity. The proposed approach will be illustrated with a case study for an IT Equipment Bid Evaluation process.
Ethics of Virtual Reality

Human beings have been spending a lot of time in front of a screen for many years. Through the computer and other electronic devices, we interact with our friends and colleagues, we maintain contact with our relatives, we carry out part of our work, we study and we carry out leisure activities. Despite the many advantages that all this has brought to our lives, ethical and social problems have also arisen. Although these electronic devices have become increasingly immersive and interactive, virtual reality seems to present a qualitative leap. In this chapter we will analyse how big this jump is, we will review the ethical problems that may appear, we will ask ourselves if they are really new and we will study how and to what extent we can face them.
Deep learning-based isolated sign language recognition: a novel approach to tackling communication barriers for individuals with hearing impairments

Sign language is a primary and widely used means of communication for individuals with hearing impairments. Current sign language recognition techniques need to be improved and need further development. In this research, we present a novel deep learning architecture for achieving significant advancements in sign language recognition by recognizing isolated signs. The study utilizes the Isolated Sign Language Recognition (ISLR) dataset from 21 hard-of-hearing participants. This dataset comprises 250 isolated signs and the x, y, and z coordinates of 543 hand gestures obtained using MediaPipe Holistic Solution. With approximately 100,000 videos, this dataset presents an essential opportunity for applying deep learning methods in sign language recognition. We present the comparative results of our experiments, where we explored different batch sizes, kernel sizes, frame sizes, and different convolutional layers. We achieve an accuracy rate of 83.32% on the test set.
Revenge Porn: On the Problem of Linguistic Analysis of Pornographic Content

The paper discusses the problem of forensic analysis of pornographic information in revenge porn situation (non-consensual public placement of materials of sexual explicit content). The paper presents a list of social practices related to revenge porn, specifies the participants of the communicative act (actor, addressee- person caught on camara, addressee-reference group, addressee-third-party participant), determines the intentional component of the actor's communicative activity, gives a list of the actor's communicative goals in conditions of multiple addressee-targeting . The features of the recontextualization of pornographic material are revealed. A weak dependence of pornographic discourse on extralinguistic background is established. It is noted that in the situation of revenge porn sexual explicit information is subjected to recontextualization, which makes it difficult to establish linguistic (semiotic) signs of pornographic information in forensic analysis. It is established that under the conditions of the transfer of a sexual explicit text into a conflict-triggering context, pornographic meanings are preserved. At the same time, the actualization of the taboo aspect of sexual interaction underlies the transformation of the emotive potential and determines the nature of the relationship between the participants in the communicative situation. The authors conclude that in order to establish the linguistic features of sexual explicit information, it is necessary to decontextualize the text submitted for forensic research. Analysis of the distribution context is necessary to establish the extralinguistic background of the actor's communication activity.