Reviewerly/exHarmony huggingface dataset:
Coffee Shop Recommendation System Using an Item-Based Collaborative Filtering Approach

To inhibit the rate of transmission of the Covid-19 virus, one of the efforts made by the Indonesian government is to impose a system of limiting social activities. Thus, resulting in changes in patterns and lifestyles in a short time. Including this “Coffee” activity. A large amount of time available due to WFH has also resulted in an increase in the number of coffee connoisseurs, including the existence of the coffee shop itself. This makes it difficult for coffee fans to choose which coffee shop is the right one to go to desire. So, a recommendation system is needed that aims to provide advice on which coffee shop to choose. The recommendation system is a system that helps users overcome overflowing information by providing specific recommendations for users and it is hoped that these recommendations can meet the wishes and needs of users. There are three types of recommendation systems based on the methods they use, namely collaborative filtering, content-based filtering, and hybrid. The method used is collaborative filtering is often used in recommendation systems. Collaborative filtering is divided into two parts, namely Item-based collaborative filtering and User-based collaborative filtering. This paper uses Item-based collaborative filtering which uses rating data between users to get recommendations. In this technique, each coffee shop that is rated by the user is checked with similar coffee shops, then combines these similar coffee shops into a list of recommendations. The test results show that the Item-based collaborative filtering method with an adjusted cosine similarity algorithm can display recommendations that are by the rating given by the customer.
EventBERT: Incorporating Event-Based Semantics for Natural Language Understanding

Natural language understanding tasks require a comprehensive understanding of natural language and further reasoning about it, on the basis of holistic information at different levels to gain comprehensive knowledge. In recent years, pre-trained language models (PrLMs) have shown impressive performance in natural language understanding. However, they rely mainly on extracting context-sensitive statistical patterns without explicitly modeling linguistic information, such as semantic relationships entailed in natural language. In this work, we propose EventBERT, an event-based semantic representation model that takes BERT as the backbone and refines with event-based structural semantics in terms of graph convolution networks. EventBERT benefits simultaneously from rich event-based structures embodied in the graph and contextual semantics learned in pre-trained model BERT. Experimental results on the GLUE benchmark show that the proposed model consistently outperforms the baseline model.
Improving the Quality of Image Captioning using CNN and LSTM Method

In image captioning improving the content of the image by describing the meaning of the picture is a challenge as it should not only be understandable to the user but also described in a short and clear sentence. The proposed solution uses CNN and LSTM as captioning models in an Encoder and the Decoder methodology is used to translate an image into a sentence. The Xception architecture is modified by adding a depth wise convolution layer. A custom activation function is created based on swish and mish. CNN is used for feature extraction, RNN is used for sequence prediction, and LSTM for framing the words into a sentence. The proposed work is validated on two-dimensional image datasets such as dog category data extracted from the flicker8k dataset and real-time images captured through a webcam. The training/testing shows improved loss value, caption prediction time, and an increase in the quality of caption in terms of the BLEU@I parameter.
Avila categorization using Machine Learning

With the rapid growth in digital media and hence the availability of abundant data, the classification problem in Machine Learning has evolved to a great extent. With the help of Classification we are able to solve the problems like distinguishing the images of animals, flowers, fruits, identifying diseases from X-ray images, predicting customer attrition, sentiment analysis, Email spam detection, credit card fraud detection etc. In this paper, we focused on the problem of predicting the copyist who worked on the transcription of the Avila Bible, a giant Latin copy of the bible, produced during the XII century. Based on the features and entrenched layout of a page, we predict the respective copyist of the scripture. Initially, we performed the exploratory data analysis to understand the data better. Following that, we conducted experiments using the Decision tree and Random forest models which gave excellent results.
LegoNet: Memory Footprint Reduction Through Block Weight Clustering

As the need for neural network-based applications to become more accurate and powerful grows, so too does their size and memory footprint. With embedded devices, whose cache and RAM are limited, this growth hinders their ability to leverage state-of-the-art neural network architectures. In this work, we propose LegoNet, a compression technique that constructs blocks of weights of the entire model regardless of layer type and clusters these induced blocks. Using blocks instead of individual values to cluster the weights, we were able to compress ResNet-50 trained for Cifar-10 and ImageNet with only 32 4x4 blocks, compressing the memory footprint by over a factor of 64x without having to remove any weights or changing the architecture and no loss to accuracy, nor retraining or any data, and show how to find an arrangement of 16 4x4 blocks that gives a compression ratio of 128x with less than 3% accuracy loss. This was all achieved with no need for (re)training or fine-tuning.
Diversifying Content Across Social Media Platforms

In this chapter, the authors describe how a committee of librarians' project to revamp the social media presence at a public comprehensive university library has helped foster deeper student engagement. By temporarily restructuring the library social media committee into subcommittees and assigning each one a single social media platform, librarians were able to develop stronger understanding of the content, norms, and audience of each platform and create more diverse and targeted content for each. This change has resulted in increased interaction with students across all platforms leading to higher attendance at library events. Preliminary findings also suggest that increased student engagement has the potential to illuminate opportunities for partnership across campus.
Actual Practicum Course vs. Virtual Living Lab in Tourism Education

As part of the curriculum at the Department of Tourism, Brawijaya University, Malang City, Indonesia, the authors are conducting a virtual tourism course to understand the interrelation of virtual reality (VR) and augmented reality (AR) in the tourism and hospitality fields. Moreover, this course also included practicums to create and use varied, immersive systems such as Web-VR, 3D VR, Multiplayer 3D VR, Marker-Based AR, and Marker-Less AR content in the context of tourism business etiquette and procedures as a virtual tour guide. In continuation, under the coordination of the tourism laboratory, they initiated a virtual living lab together with students and stakeholders that focus on strengthening Malang's City identity as a heritage city by using AR/VR technologies. Therefore, this chapter aims to define and recognise basic insights of the concept, types, and characteristics of remote practicum methods. Specifically, this review also describes with examples required equipment and best practice software and explains the advantages and limitations that may occur.
Procurement 4.0 and Organizational Performance in the Malaysian Construction Industry

Procurement 4.0 is the result of the industrial revolution (IR) 4.0 technologies implemented into the organization. The incorporation of IR 4.0 with the procurement system in the Malaysian construction industry is still low because of the lack of expertise available to adopt the technology. Therefore, this study investigated relationships between procurement 4.0 elements, organizational performance, and mediating effect of intention to optimize the procurement process. To support this study, resource-based view theory and theory of planned behaviour were applied. This study focuses on 5267 G7 Malaysia construction companies. 413 online survey questionnaires were distributed using cluster random sampling techniques, and 134 responses were successfully acquired, representing 32.45% of the response rate. SmartPLS was used to analyze the data. Seven hypotheses are supported, while three are not supported. The mediating effect of intention to optimize the procurement process is significant only in the relationship between procurement 4.0 strategy and organizational performance.
Encrypted DNS Traffic Analysis for Service Intention Inferring

Service intention refers to what service or which service the server provides. The former includes service classification, service type, or service behavior classification. The latter contains service content classification, such as shopping online or uploading and downloading, etc.. Port-based classification and payload-based classification are two widely used service classification schemes, both of which have many limitations, such as only focusing on server-side scenarios or just designing for non-encrypted requests. In this paper, we propose an encryption-independent approach from a network-side perspective by analyzing the communication behavior of the IPs. Firstly, we identify similar service behavior clusters by employing service influence metrics. Then, we devise a semantic mining mechanism to infer whether they serve a fixed user group or provide interactive service. Finally, we use open-source benchmark datasets, synthetic datasets, and the real Netflow data collected from the China Education Research Network backbone (CERNET) to verify our proposal. Experimental results demonstrate that the accuracy and recall rate of the proposed approach is better than other similar state-of-the-art methods. Besides, our work can also distinguish malicious behavior clusters. Extensive experiments demonstrate that our work is efficient for network management and security monitoring.
Mobile Learning Strategy Based on Principal Component Analysis

Mobile learning is a kind of learning mode by using mobile devices, and it is an indispensable way of learning strategy in colleges and universities. The authors conducted the interviews and questionnaires about the teaching situation, learning strategies, using of network resources, and so on. Next, the authors checked and verified carefully the feedback data from classroom teaching. In the process of investigation, the students were divided into two groups. The authors analyzed the mean and standard deviation of the two groups of data tables. According to the data reliability analysis, exploratory factor analysis, significance analysis, the authors propose the teaching mode of “one heart, two sides and six links(OHTSSL)” based on mobile learning strategy. In order to construct new cognitive content and train students' innovation ability, teacher and students must implement the mobile learning strategy in classroom teaching. Teacher and students execute teaching process of six links based on OHTSSL teaching mode.
A Boundary-Aware Network for Shadow Removal

Shadow removal is a challenging computer vision and multimedia task that aims to restore image content in shadow regions. The state-of-the-art shadow removal methods introduce artifacts near shadow boundaries or inconsistencies between shadow and nonshadow areas, which can be easily noticed by the human eye at first glance. In this paper, we design a boundary-aware shadow removal network (BA-ShadowNet) that improves shadow removal accuracy by increasing the removal performance at shadow boundaries. In contrast with previously developed methods, which usually consider shadow boundary optimization to be a postprocessing technique, our method performs shadow removal and shadow boundary optimization simultaneously. For this purpose, the proposed BA-ShadowNet is designed as a multiscale encoder-decoder structure, where the decoder consists of a shadow removal branch and a shadow optimization branch. An interaction module is then introduced to fuse and exchange the features of the two branches. This module facilitates the removal branch in perceiving the locations and colors of shadow boundaries. Additionally, it optimizes the boundary branch according to the image context extracted from the removal branch. A three-term loss function is further developed to supervise the shadow removal results and to address the issue of imbalanced supervision between shadow boundary pixels and pixels inside shadows. Extensive experiments conducted on the ISTD+ and SRD datasets demonstrate that the proposed BA-ShadowNet greatly outperforms the state-of-the-art methods with respect to shadow removal.
Recording Behaviors of Artificial Intelligence in Blockchains

The key challenges of current blockchain, such as low throughput, high latency, and poor scalability, hinder its future development. The recent advances in artificial intelligence (AI) can well overcome these challenges, thus spurring an increasing number of organizations and individuals to equip blockchain with AI, making blockchain become more intelligent. However, AI behaviors need to be supervised for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex-post</i> forensics in case of dispute and accountability. This motivates us to envision a model to record the AI behaviors in intelligent blockchain. In this article, we propose such a model called AI-Tracer, which leverages proxy re-encryption based on Schnorr signature as well as InterPlanetary File System (IPFS) to be integrated into intelligent blockchain. AI-Tracer generates AI-digest during the AI learning process and encrypts it with proxy re-encryption. IPFS is responsible to store the encrypted AI-digest and returns a hash pointer related to AI-digest for further verification. The authorized user can verify the validity of AI-digest by leveraging advanced features of blockchain technology and only the valid AI-digest can be uploaded to intelligent blockchain, which implements trusted tracking for AI behaviors. AI-Tracer achieves fine-grained access control of AI-digest via proxy re-encryption. A user can retrieve the plaintext of AI-digest from intelligent blockchain with specific authorization. We conduct theoretical analysis to indicate the privacy and security of AI-Tracer. Moreover, we deploy AI-Tracer on the Hyperledger Fabric and demonstrate the efficiency and effectiveness of AI-Tracer through extensive experiments.
XSHM: Proposed Hybrid Process Modeling Technique from Scrum and XP for PSP and Medium Projects

In this paper I have tried to propose and new hybrid model of XP and Scrum already existing models and naming XSHM, It is a proposed model for agile software development industry. The proposed model is helpful for agile software development for the development of personal software process and medium software development projects. This model has the flexibility to adopt strong and useful features of both scrum and extreme programing models and skip or cutoff all the unsuitable and less affective modules of both model. This model may have Advantage and disadvantages by adopting this technique. I did so to make it clear for the people who are confused while choosing any modeling technique in some situations of agile development. I have not only discussed their working of XP and scrum in agile but also their advantages and disadvantages as well. After reading this paper, reader will be able, understand the working of both XP, scrum models in agile techniques, and be able to choose the XSHM in their useful situation. It will also clear the working and importance of software development of PSP and medium software projects using proposed model. I have tried my best to explain techniques used in XSHM in this paper so that reader may be able to use it and aware about its importance and choose the most suitable one for his situation.
Designing and Implementing Ecological Models in Organisation Studies: Fuzzy Cognitive Mapping Approach (FCM)

This chapter proposes the Fuzzy Cognitive Mapping (FCM) approach as a useful methodological tool in studying the complex phenomenon of workplace spirituality. The FCM approach allows researchers to estimate and visualise causal relationships that reflect complex decision-making processes, especially in the face of adversity. More specifically, various coping mechanisms are examined in workplace spirituality research in relation to organisational resilience and sustainability. However, little evidence is offered of whether and how the various coping mechanisms interact with each other and structure complex decision-making problems, resulting in either productive or non-productive outcomes. I suggest that this is because there is no accessible template for employing the technique with the types of data researchers tend to encounter. The aim of this chapter is to provide an overview of the FCM approach and some practical recommendations on how scholars can employ FCM by using two examples studying organisational resilience. Implications for future research are considered.
Vocabulary enhancement in Chinese-named entity recognition

In the traditional Chinese-named entity recognition system, the word-based sequence labeling model is affected by the effect of word segmentation, which is easy to cause entity boundary detection errors. Although the character-based sequence labeling model avoids the error propagation of the word segmentation system, it loses a lot of lexical information because its model can only learn the original language signals at the character level. This leads to the blurred boundary of the entity and the poor effect of entity recognition. In order to solve the problem that it is difficult to demarcate the boundaries of Chinese-named entities, a vocabulary enhancement model is proposed. First of all, the model starts from the character-based sequence labeling model to avoid the error propagation of Chinese word segmentation. Then, it is integrated into the external lexicon to increase the lexical information and improve the entity boundary. Finally, the ERNIE pre-trained language model is introduced to supplement the hidden vocabulary features and improve the contextual information capture ability of words. Therefore, the model has a strong semantic awareness, which significantly improves the effect of Chinese-named entity recognition in each classical data set.
Study on Disturbance Propagation Characteristics of Power Grid and Evaluation of Temporal and Spatial Correlation Characteristics of Voltage Phase Trajectory

With the access of large-scale new energy and power electronic equipment, as well as the influence of various random uncertain factors, the analysis and control of dynamic security of large power grids become particularly difficult, so it is of great practical significance to study the disturbance propagation characteristics of complex power grids. Voltage is an important parameter that affects the security of power systems, and the voltage phase trajectory contains abundant power grid information. Firstly, based on the measured data, this paper quantitatively describes the characteristics of dynamic space-time distribution of the voltage, and numerically reveals the different influences of disturbance on different nodes and the characteristics of disturbance propagation. Then, the geometric characteristics of the measured voltage phase trajectory are mined, the new trajectory distance is defined, the characteristic trajectory plane is constructed, and the spatio-temporal evolution law of voltage phase trajectory is characterized. In the sequel, through the density-based DBSCAN clustering method, the characteristic tracks of voltage movement are clustered and analyzed, and the temporal and spatial correlation characteristics of voltage movement trends of each node are measured, and the law of disturbance propagation is discovered. Finally, based on the measured data of IEEE 39-bus system, the simulation analysis is carried out, and the effectiveness of the presented algorithm is verified.
Object-Detection Based Recommendation Engine for Advertising Using Deep Learning

With the exponential increase in online advertising, it has become increasingly important to determine new and innovative methods to identify the right advertisement to display to the consumer. Current methods of recommendations for advertisements employed by popular streaming platforms use the implicitly and explicitly collected data of the users for recommending advertisements. These recommendations may not always be accurate, and a user could be bogged down by a huge number of ads from irrelevant domains. Our research focuses on a novel approach for advertising which utilizes object detection for recommending advertisements. In its current state, this idea is based on the frequency of objects detected in the frames of the video. The main outcome was that our recommender engine performed better in terms of the relevancy of the advertisement, when compared to existing systems, most notable of which is YouTube. We also note that the privacy of the user is also improved, since their personal data is not being collected in order to recommend advertisements. In terms of future scope, we identify some key areas of improvement, such as the further classification of the objects detected into sub domains, making for more fine-tuned recommendations, as well as factors involving the selection of videos such as quality, duration, and relevance.
FairFuse: Interactive Visual Support for Fair Consensus Ranking

Fair consensus building combines the preferences of multiple rankers into a single consensus ranking, while ensuring any group defined by a protected attribute (such as race or gender) is not disadvantaged compared to other groups. Manually generating a fair consensus ranking is time-consuming and impractical- even for a fairly small number of candidates. While algorithmic approaches for auditing and generating fair consensus rankings have been developed, these have not been operationalized in interactive systems. To bridge this gap, we introduce FairFuse, a visualization system for generating, analyzing, and auditing fair consensus rankings. We construct a data model which includes base rankings entered by rankers, augmented with measures of group fairness, and algorithms for generating consensus rankings with varying degrees of fairness. We design novel visualizations that encode these measures in a parallel-coordinates style rank visualization, with interactions for generating and exploring fair consensus rankings. We describe use cases in which FairFuse supports a decision-maker in ranking scenarios in which fairness is important, and discuss emerging challenges for future efforts supporting fairness-oriented rank analysis. Code and demo videos available at https://osf.io/hd639/.
A Comparative Study of Deep Convolutional Neural Networks for Car Image Classification

This paper aims to present the result of a comparative study of Deep Convolutional Neural Networks (CNN) using the AlexNet architecture to use the car image classification of a small dataset. We have proposed the experiment result from a comparative study dropout value using Cuckoo Search (CS), of the optimization techniques for a small data set solving problem of overfitting. The car images for the experiment are different in color, size, and position. As a result, the training time average of $\sim 59.16$ minutes, and the model accuracy of 91.41%.