Reviewerly/exHarmony huggingface dataset:
Career-Based Explainable Course Recommendation

The type and quality of education that a student receives can have a profound impact on their career. In contrast to education that is not intentionally organized to help students achieve specific career objectives, career-based education seeks to provide students with the skills that are required to achieve specific career goals. In this work, we propose a course recommendation framework that is designed to recommend courses based directly on their ability to teach skills relevant to a user-specified career goal. Within our framework, course recommendations are generated in a transparent manner, using skills to bridge between jobs and courses in a knowledge-based inference procedure. Due to the procedure’s transparency, our system is able to provide faithful template-based explanations detailing why each recommended course was chosen for recommendation. Our framework contrasts with other course recommendation systems in the literature that lack the ability to explain their choices and therefore may lack trustworthiness from the perspective of a user. The proposed framework has several applications, including assisting students with course planning, as well as aiding with curriculum evaluation and development by providing insight into the usefulness of specific courses to specific careers. We conduct a preliminary evaluation of our system, and its performance is competitive against two baselines. We provide all the resources needed to reproduce our results. ( http://github.com/striebel/cbecr )
Deep Fourier Kernel Exploitation in Blind Image Super-Resolution

Blind image super-resolution (SR) has achieved great progress through estimating and utilizing blur kernels. However, current predefined dimension-stretching strategy based methods trivially concatenate or modulate the vectorized blur kernel with the low-resolution image, resulting in raw blur kernels under-utilized and also limiting generalization. This paper proposes a deep Fourier kernel exploitation framework to model the explicit correlation between raw blur kernels and images without dimensionality reduction. Specifically, based on the acknowledged degradation model, we decouple the effects of downsampling and the blur kernel, and reverse them by the upsampling and deconvolution modules accordingly, via introducing a transitional SR image. Then we design a novel Kernel Fast Fourier Convolution (KFFC) to filter the image feature of the transitional image with the raw blur kernel in the frequency domain. Extensive experiments show that our methods achieve favorable and robust results.
Blindly Evaluate the Quality of Underwater Images via Multi-perceptual Properties

The quality of underwater images can vary greatly due to the complexity of the underwater environment as well as the limitations of imaging devices. This can have an effect on the practical applications that are used in fields such as scientific research, the modern military, and other fields. As a result, attaining subjective quality assessment to differentiate distinct qualities of underwater photos plays a significant role in guiding subsequent tasks. In this study, an effective reference-free underwater image quality assessment metric is proposed by combining the colorfulness, contrast, sharpness, and high-level semantics cues while taking into account the underwater image degradation effect and human visual perception scheme. Specifically, we employ the low-level perceptual property-based method to characterize the image’s visual quality, and we use deep neural networks to extract the image’s semantic content. SVR is then used to create the quality prediction model by analyzing the relationship between the extracted features and the picture quality. Experiments done on the UWIQA database demonstrate the superiority of the proposed method.
Identification of Key State Information of Substation Equipment Based on Text Mining and Semantic Analysis Technology

As the number of substations in the power system continues to increase, the management of power equipment becomes increasingly complex. Using image recognition technology to perform text recognition on power equipment nameplates can effectively improve the management level of power system equipment. Text detection is the first step in text recognition, and traditional text detection techniques are complex and time-consuming. This paper proposes a nameplate text detection model based on the deep learning model EAST. Experiments show that this method can greatly accelerate the efficiency of power equipment nameplate recognition.
It’s Always Good to Be in the Game

Finally, some words about losing, giving up, surrendering, when we have no more arguments, possibilities, chances, and we need to come to terms with the reality. Unfortunately, there will be situations when the supplier is offended, destroyed, or blatantly cheated. Some clients will use unfair tricks, enforce extra work, make additional quality demands, deliberately prolong the approval process, blackmail, postpone signing the protocols despite all the work being done, indefinitely delay payments. There will be attempts at playing some unfair business games like enforcing discounts in fees guaranteed in the contract (either for the project or system support), attempts at optimizing the maintenance fee to lower the overall costs of using the system with an increasing number of users. Clients will introduce new subcontractors that will take over the maintenance and development of your system, they will expect your support in implementing the strategy that is against your interest! They will insist on getting the source code to take control of the system and get rid of your company. They will demand services that will shorten the period of using your products. Your system will be slowly shut down and that will eventually significantly limit your business with the customer.
Information Retrieval Research in Academia and Industry: A Preliminary Analysis of Productivity, Authorship, Impact, and Topic Distribution

Due to the increasingly diversified research topics, approaches, and applications, there is a growing trend of scientific collaboration between academia and industry in information retrieval (IR) research. However, the characteristics in productivity, authorship, impact, and topics for publications by researchers from academia and industry and by academia-industry collaboration still remain understudied. In this paper, we examine the features and differences regarding productivity, authorship, and impact of the three types of studies and also pay special attention to the research problems and topics that attract and foster academia-industry collaborations in the recent two decades of IR studies. To this end, we analyzed 36,072 research papers published by 52,419 authors from 2000–2021 in the field of IR from ACM Digital Library. We find that the three categories have clear preferences in terms of selecting which academic conferences for publication. Regarding author teams, the industry community prefers small teams or solo-authored publications compared with the academic community. As for impact, papers by academia-industry collaboration tend to have higher citation impact compared with the research where only one community is involved. The thematic analysis of academia-industry collaborative papers and co-authorship network analysis reveal the preferred choice of research topics and the continuous “centrality” of researchers from academia. Knowledge from the study offers a new perspective for analyzing the advance and emerging trends in IR research and further clarifies the cross-community collaborations and scientific contributions of academia and industry.
API Portfolio and Framework

This chapter starts with the API portfolio architecture and then gets into the framework for API development. An overview of the API framework starting from the client to data is discussed, and then the focus is shifted to review the services layer with an exercise implementing the services layer.
API Platform and Data Handler

This chapter starts with API Platform architecture and then gets into the data handler pattern for the integration of RESTful APIs with actual data sources within an enterprise to make it more meaningful to the consumers through APIs.
API Design and Modeling

This chapter starts with API design strategies and then goes into API creation process and modeling. Best practices for REST API design are discussed, followed by API solution architecture. In the exercises, a simple API is designed for podcast subscription and then modeling using OpenAPI.
Test Doubles

A test double is a special type of function (or object) that replaces a dependency of the structure under test with a “blank” version, which avoids doing any of the real work of the dependency.
The Whole Truth About User Acceptance Tests

You could imagine a perfect situation when there’s no time pressure from the client, there is no emphasis on annual performance and invoicing, and the whole project can be executed by the book, following the best practices of the IT trade. Then we would have time to develop the commissioned system so that it fulfills all the client’s wishes and to perform all the work related to achieving the desired quality. We would probably spend a lot of time adjusting the system’s performance under real-life load conditions, deliver the non-functional requirements, etc. In theory, approval procedures of such a system would be just a formality. The client would conduct user acceptance tests quickly, there wouldn’t be any major bugs, after that the system could go straight to production.
Few-Reference Image Quality Assessment with Multiple Information Measurement Fusion

In the era of media information explosion, there is an urgent need for a fast and reliable image quality assessment (IQA) model to improve the actual application effect of images. To this end, we propose the multiple information measurement fusion metric (MMFM), which innovatively combines two types of information measures (IMs), i.e., local IM and global IM, using only a small number of references for IQA. First, inspired by the free energy theory, we combine 2-dimensional autoregressive model with sparse random sampling method as an inference engine on an input image to generate its associated predicted image. Second, by the inspiration of pixel-wise measurement, we obtain the local IM by calculating the information entropy of the residual error between the input image and its corresponding predicted one. Third, motivated by the histogram-based measurement, we acquire the global IM by computing the two kinds of divergences between the input image and its predicted one. Fourth, we systematically fuse three components, independently including one distance of local IM between the reference and corrupted images and two distances of global IMs between the reference and corrupted images, based on a linear function to derive the final IQA result. The results of experiment on the most popular LIVE database show that our designed algorithm with only one number used as few reference has achieved well performance as compared with several mainstream IQA models.
Pedestrian Re-recognition Based on Memory Network and Graph Structure

Computer interaction and public safety have great research significance and practical value. Because of the problem that the recurrent neural network used in the existing literature will produce gradient disappearance and gradient explosion when the video sequence is long. Then, we propose a Person Re-identification Network with ConvLSTM (CLPRN) network based on convolutional long and short-term memory networks to solve the short-term memory problem. And then, aiming at the problem of information fusion between frames, we propose a Person Re-identification Network with Graph Convolution (GCPRN) network based on the graph structure, introduce a multi-header attention mechanism, and measure the relationship between frames. The experimental results shows that the Rank 1 of the GCPRN network on iLIDS Video re-identification (iLIDS-VID) dataset reaches $$70.58\%$$ and Rank 5 reached $$81.20\%$$ , surpassing the Unsupervised Tracklet Association Learning (UTAL) and Temporal Knowledge Propagation (TKP) algorithm that reached a high level on the iLIDS-VID dataset.
Adversarial Example Defense via Perturbation Grading Strategy

Deep Neural Networks have been widely used in many fields. However, studies have shown that DNNs are easily attacked by adversarial examples, which have tiny perturbations and greatly mislead the correct judgment of DNNs. Furthermore, even if malicious attackers cannot obtain all the underlying model parameters, they can use adversarial examples to attack various DNN-based task systems. Researchers have proposed various defense methods to protect DNNs, such as reducing the aggressiveness of adversarial examples by preprocessing or improving the robustness of the model by adding modules. However, some defense methods are only effective for small-scale examples or small perturbations but have limited defense effects for adversarial examples with large perturbations. This paper assigns different defense strategies to adversarial perturbations of different strengths by grading the perturbations on the input examples. Experimental results show that the proposed method effectively improves defense performance. In addition, the proposed method does not modify any task model, which can be used as a preprocessing module, which significantly reduces the deployment cost in practical applications.
On the Robustness of “Robust Reversible Data Hiding Scheme Based on Two-Layer Embedding Strategy”

In the paper “Robust reversible data hiding scheme based on two-layer embedding strategy” published in INS recently, Kumar et al. proposed a robust reversible data hiding (RRDH) scheme based on two-layer embedding. Secret data was embedded into the most significant bit-planes to increase robustness, and a sorting strategy based on local complexity was adopted to reduce distortion. However, Kumar’s reversible data hiding (RDH) scheme is not as robust against joint photographic experts group (JPEG) compression as stated and can not be called RRDH. This comment first gives a brief description of their RDH scheme, then analyzes their scheme’s robustness from the perspective of JPEG compression principles. JPEG compression will change pixel values, thereby destroying auxiliary information and pixel value ordering required to extract secret data correctly, making their scheme not robust. Finally, the changes in both bit-plane and pixel value ordering after JPEG compression are shown and analyzed by different robustness-testing experiments.
ViT-Siamese Cascade Network for Transmission Image Deduplication

With the large-scale use of various inspection methods such as drones, helicopters, and robots, the generated power inspection images have increased significantly, which has brought huge pressure on data storage and transmission. At the same time, with the rapid development of artificial intelligence technology in the electric power field, the cost of manual labeling required for model training has become a major pain point. This paper studies a transmission image deduplication technology based on ViT-Siamese cascade network, which reduces the amount of data and the cost of data annotation. This paper first investigates the research status of image similarity at home and abroad, and then studies the transmission image deduplication technology based on the ViT-Siamese cascade network, which greatly reduces the complexity of similarity calculation, and finally trains the model on the transmission image data set. Firstly, this paper investigates the research status of image similarity at domestic and international. Next, transmission image deduplication technology based on ViT-Siamese cascade network is studied, which greatly reduces the complexity of similarity calculation. And finally, the AI model is trained on transmission image dataset, and effectiveness and feasibility of the technology in transmission scene processing are verified by experiments.
FPGA-Based Hardware Implementation of JPEG XS Encoder

JPEG (Joint Photographic Experts Group) XS is a new international standard targeting mezzanine compression with features of low complexity and low latency. It is designed extremely parallelizable and simple to implement on modern CPU, GPU, FPGA and ASIC. This paper proposes an efficient hardware JPEG XS encoder implementation. In this design, JPEG XS encoder is clearly decoupled into sub-modules that are pipelined and parallelized to effectively increase system throughput. The carefully crafted Rate Control module takes up a few cycles while reducing the use of hardware resources. The well-designed hardware architecture is suitable to be implemented using Xilinx HLS Tool and operates at 196 MHz on Alveo U50, achieving encoding speed of 8K 42 fps. The restored images usually yield a PSNR over 50 dB when bitrate is larger than 8.0 bpp.
Sentiment Analysis of Customer on a Restaurant Using Review in Twitter

As it is important to maintain the customer relationship the business firms like restaurants, Apps, etc., should take the opinion of the users and improve from the feedback they will give to the company and this improves the relationship with the customer promising his/her regular visits to your businesses. In this research, we will see the results of using natural language processing in analyzing the restaurant reviews dataset and by which categorization into positive and negative reviews restaurant owner can save his time by taking only the negative reviews into consideration and can improve their businesses.
Design and Implementation of Security Enhancement for Trusted Cloud Computing

The concept of cloud computing is providing dynamic, scalable resources that are delivered over the Internet. Access to remote computer resources is made available to users through this service, and they only pay for the services that they actually use, at the time that they use them. However, for cloud users, the security of the information that is stored in the cloud is the most important concern that they have regarding cloud computing services. As a result of its ability to provide customers with on-demand, flexible, dependable, and low-cost services that are also scalable, cloud computing has experienced rapid growth in recent years. This invention relates to the design and implementation of an algorithm to improve cloud security with the goal of ensuring the security of information at the point of storage in the cloud. For a data security concept that emphasizes increased confidentiality and authenticity for cloud data at the cloud storage end, as well as experiment analysis to verify the approach’s effectiveness and efficiency, a security strategy is established and developed.
Tree-Based Credit Card Fraud Detection Using Isolation Forest, Spectral Residual, and Knowledge Graph

Credit card frauds have constantly been a significant threat to the worldwide economy since the advent of non-cash payments, leading to high demand for fraud detection research. Almost existing studies on supervised credit card fraud classifiers cannot leverage domain characteristics such as fraud rings and sudden changes in payment traffic. They also often fail to detect unseen fraud patterns. In this paper, we introduce a set of features, targeting both traditional and modern fraud patterns, generated from Isolation Forest, Spectral Residual, and Knowledge Graph to boost the performance of tree-based models. We evaluate different tree-based models on the Kaggle public dataset and demonstrate the enhancement of our feature scores in the enterprise payment context. The proposed feature set increases the AUC-ROC and AUC-PR of XGBoost by up to 4.5% and 47.89%, respectively, on the enterprise dataset with only 0.0028% of fraud samples. Our results suggest a method for further improvement on a wide range of fraud detection problems based on domain analysis and feature extractors, especially Knowledge Graph.
Success factors for agile adoption in one of the ministries in Indonesia

The Information and Communication Technology (ICT) project is an investment in software development, data migration, and infrastructure development carried out by the organization to achieve value or benefit for the organization. The ICT project can be developed using a traditional approach but must be rigid and systematic so that it does not support the dynamics of current changes such as volatility, uncertainty, complexity, and ambiguity (VUCA). The ICT project development approach following the VUCA is an agile approach, where the project will be managed iteratively and incrementally so that the organization can receive the project value quickly and accurately. Unfortunately, not all organizations can accept and deal with this change and switch from traditional to agile development, especially for government organizations, where the nature of the organization is more bureaucratic than agile, thereby increasing the challenges in agile adoption. Therefore, this research is focused on determining the success factors carried out by organizations in adopting agile. Agile adoption success factors were obtained from a literature review, which resulted in 36 factors from 5 dimensions which experts then verified through online surveys. The survey was analyzed using the Fuzzy Delphi method with factor acceptance indicators based on the defuzzification value used for factor ranking, the threshold value, d-construct ≤0.2, and the expert consensus value ≥75%. The analysis results with the Fuzzy Delphi method produce 4 discarded factors, 32 retained factors, and a ranking for each factor. The discarded factors and the success factors with the highest ranking, the factors that fall into the defuzzification value range of 0.680 to 0.800, were analyzed more deeply by using the interview method to the expert. The 10 highest success factors were found and analyzed more deeply by experts to be used as recommendations for agile adoption success factors that can be used in government. The success factors are management support, management commitment, culture of expressing opinion, adaptive nature and involvement of project leaders, independent team, focus on customer needs, focus on agile requirements, deliver project results on a regular basis, prioritizing important feature development, and projects that have a small team.
Augmented reality-based serious game using ADDIE model to support reading activity

Reading skill is the main factor to succeed in learning activity. Reading skill can be improved through intense and continuous practice. To support learning, students must be accustomed to reading early in their life. However, this is not easy to achieve. A learning system is needed to motivate students to support their reading activity. This research is conducted to develop a learning system that can attract student's motivation to read. The use of learning tool was our main focus in developing the learning system offered in this research. We utilized augmented reality technology to attract student's interest and serious game technology to create challenges in the learning process. The result of the test which was directed toward teachers and students concluded that augmented reality technology developed in the form of serious game can increase student's motivation and assist teachers in controlling the learning process. In developing the learning system, we used the ADDIE (Analysis, Design, Development, Implementation, Evaluation) model approach. We assume that the phases in the ADDIE model is adaptive to the processes that we have done in this research. This research made a significant contribution of using the ADDIE model approach to develop a serious game in the education field. Based on our experience, the highly committed parties involved (researchers, teachers, and students) play the main role in supporting the success of ADDIE model approach in this research.