Reviewerly/exHarmony huggingface dataset:
Building Adaptive Software Reusable Components Using Domain Engineering

Building Adaptive Software reusable components are one of the main advantage of Component Based Software Engineering (CBSE). The long-term benefits of an exhaustive domain analysis is that captures the requisites of past, as well as future systems within the domain development of reusable software components and supports the application specific development of the domain. Software developers can use a plug and play approach to facilitate the development and integration of software reusable components. Software reuse procedures and processes should be integrated into the existing software development process, so that software asset library should be created and maintained, so that they will contribute to the design and reuse of software assets. This paper specifies the utilization of software reuse principles, domain engineering techniques, process architecture, process modeling mechanisms and project-categorical processes can be abstracted into reusable components that can be utilized by process engineers.
A Design of Electricity Trading System Based on Blockchain Technology

The traditional power trading system is mainly based on a centralized architecture, and there are many problems such as many participants, insufficient data security, and data islands, which limit the willingness of trading entities to enter the market. Blockchain technology has the characteristics of openness, transparency, traceability, non-tampering, weak centralization, etc., which to a certain extent helps to solve the problems existing in traditional power trading systems. This paper proposes an architecture design of a power trading system based on blockchain technology, analyzes the block link access mode, and provides a new idea different from the traditional mode of distributed storage, hybrid deployment and flexible access. Problems such as data islands and insufficient security in power transaction scenarios have improved the flexibility of data access.
Facial feature point recognition method for human motion image using GNN

To address the problems of facial feature point recognition clarity and recognition efficiency in different human motion conditions, a facial feature point recognition method using Genetic Neural Network (GNN) algorithm was proposed. As the technical platform, weoll be using the Hikey960 development board. The optimized BP neural network algorithm is used to collect and classify human motion facial images, and the genetic algorithm is introduced into neural network algorithm to train human motion facial images. Combined with the improved GNN algorithm, the facial feature points are detected by the dynamic transplantation of facial feature points, and the detected facial feature points are transferred to the face alignment algorithm to realize facial feature point recognition. The results show that the efficiency and accuracy of facial feature point recognition in different human motion images are higher than 85% and the performance of anti-noise is good, the average recall rate is about 90% and the time-consuming is short. It shows that the proposed method has a certain reference value in the field of human motion image recognition.
Considerations for Indigenous Cultural Aspects in Software Development: A Case Study

Requirements engineering (RE) is a human-centric domain and requires broader consultations. Oftentimes, focus of the process is on the software development perspectives and fails to consider how users view the requirements engineering process. This research examines whether cultural influences are important in the software development requirement engineering process from the users’ perspective. A case study to elicit user requirements was conducted using design thinking and a human-centered approach, with data collected from university students from Papua New Guinea (PNG) and other Pacific Island nations. The findings reveal 11 cultural characteristics distinct to the indigenous cultures of participants have an impact on RE activities; six are related to Hofstede’s cultural dimensions, while five are unclassified and unique to PNG. The study highlights the importance of culture in the RE process and why it is essential to consider users’ cultural expectations in software development.
Contributions of Blended Teaching Mode to Interdisciplinary Education in the Post-Covid-19 era

The rapid spread of the new crown pneumonia epidemic on a global scale has rapidly expanded the application scope of online teaching models. The blended teaching mode which combine online and offline teaching has become an important teaching method for higher education in the Post-Covid-19 era. This article uses questionnaire survey and interviews to investigate a double-class university in China. And it analyzes the contribution of blended learning to break through the barriers of disciplines and realize the cultivation of interdisciplinary talents. It found that, firstly, with the help of online seminars, interdisciplinary teacher teams can increase the frequency of interaction with each other, and in this way, the quality of course content has been improved. Secondly, with the help of online and offline teaching methods, the curricula can accommodate teachers from more disciplines to participate in the evaluation of students. Thirdly, affected by the epidemic, many schools and institutions have set up a large number of courses as open online courses to enrich the ways and content of interdisciplinary learning. But at the same time, we should also face up to the shortcomings of blended teaching in order to continuously improve the quality of teaching.
Poly: Shape-changing Conversational Agent Helps Identify Multiple Characters in Storytelling

In recent years, storytelling has attracted attention as a possible important application context for CAs. However, it is difficult to follow and recall the story flow by distinguishing their lines since most story content features multiple characters. We introduce Poly, a physically actuatable CA that implements a 2.5D shape display to help identify multiple characters in a story. To evaluate the effect of adding a novel approach of physical actuation into the conventional color expression technique, we conducted a 2×2 within-subject user study with two levels of shape change (shifting shapes by characters or not) and two levels of color change (shifting colors by characters or not) in a studio setting. The quantitative and qualitative analysis results show that shape change has a significant and exclusive effect on both user acceptance of Poly and narrative engagement of the story, although there is no interaction effect according to color change and shape change.
Exploring Embodied Gestures and Video Filters for More Expressive Virtual Group Meeting Interactions

This paper explores my doctoral research work on supporting how people express their embodied virtual meeting interactions through the synthesis of physical gesticulations and video filters. The goal is to suggest novel interaction methods that first capture the intent from people's communicated gestures, and then express those intentions more effectively to other virtual meeting attendees as informative video filter responses. By combining interpreted physical gestures as input and informative video filters as output, I hope my research can bring a greater dimension in how people express their embodied interactions beyond the constraints of current virtual meeting environments.
Implementation of the Bresenham's Algorithm on a Four-Legged Robot to Create a KRPAI Arena Map

Kontes Robot Pemadam Api Indonesia (KRPAI) is a division of the Robot Competition in Indonesia whose mission is to extinguish fires according to the rules of the Trinity College International Fire Fighting Robot Contest (TCIFFRC) in Hartford, United States. The robot is placed in one of the 4 rooms randomly and is required to find a fire then extinguish it and return to the room where the robot is placed. The ability of robots to be able to carry out missions perfectly often fails because of the disturbance of obstacles in the room, do not know the pose of the robot when outside the room. A map is very important so that the robot knows its position in space and the distance of the robot from obstacles. Therefore, the focus of this research is not only on simulations but also on direct map-making to dynamically moving four-legged robots. The odometry method on the four-legged robot is used for routing and localization algorithms in making maps. Bresenham's algorithm is implemented in the map creation process. The results of the test and analysis show that a 1 mm grid map to a 5 mm grid map can describe parabolic-shaped obstacles, and the average error cell value for corridors, door widths, and walls is 0.92 cells.
Overcoming the Coordination Problem in New Marketplaces via Cryptographic Tokens

We study the use of platform-specific tradable cryptographic tokens to solve the coordination problem that is common in adopting new marketplaces. We show that certain characteristics of platform-specific tokens --- specifically their tradability, the ability of the platform to commit both to accept and to require these tokens in the future, and the ability to commit to a price and/or quantity schedule --- can help overcome the coordination problem and can support equilibria favorable to the new marketplace. Compared to other mechanisms in the literature to address the coordination problem --- such as subsidies of early users and promises of refunds or buy-backs if the platform fails --- platform-specific cryptographic tokens are less likely to favor established or larger firms with established reputation and financial resources.We find that tokens allow a marketplace to trade-off future revenue for present revenue, which then can be used to address the coordination problem. On the other hand, the ability to trade out of the platform results in a smaller future network and lower total profit. Thus, if the new marketplace is not facing capital constraints, the most profitable strategy is the traditional strategy to subsidize adoption. If the new marketplace is capital constrained, however, as is often the case for new entrants and unproven technology applications, tokens can offer an alternative that is increasingly attractive as the cost of capital increases.
Social media, AI, and political affiliation

Twitter is one of the famous micro-blogging websites, currently has 309 million active users. Most famous people, including political leaders, uses Twitter to disseminate information to their followers. People retweet the information shared by the political leaders, which expose their political affiliation to the public. Moreover, this re-sharing could impact the personal and social life of the users. This paper aims to identify the polarity (support/opposition) of the tweets against the current government. For this purpose, a Convolutional Neural Network (CNN) based model is proposed to identify the polarity. We conducted our experiments on 10,000 tweets collected from 60 different political users accounts. Compared to the other techniques with the various hyperparameters configuration, our proposed model outperforms the baseline machine learning models.
Design and Implementation of Course Selection System based on SpringCloud Micro-service Architecture

With he continuous reform of the teaching system, especially the expansion and deepening of the credit system and elective course system, the daily management of the teaching administration is heavy and complicated. How to informationize, modularize and facilitate the teaching administration is the focus of the development of modern universities. In the process of the gradual application of digital theory, the student course selection system is faced with many difficulties, such as large base, large data and frequent operation, which need to be optimizedThe system mainly adopts SpringCloud micro-service solution as the system architecture, MySQL database as the development platform, and Java language to write programs to complete data input, search, change and delete operations to complete data storage.
Relative clause extraposition and PP extraposition in English and German

Abstract Chapter 6 applies theoretical concepts from earlier chapters to the interpretation of corpus and acceptability judgment data from several studies of relative clause extraposition and PP extraposition in English and German. It is shown that assumptions about form–meaning isomorphism influence how grammatical constraints that have a semantic component are interpreted, while assumptions about gradient grammaticality influence how lower acceptability ratings of structures that naturally occur in corpora are interpreted. It is further shown that by considering corpus data and data from elicited production tasks, it is possible to narrow down the range of plausible explanations for a given contrast in acceptability. Finally, it is argued that certain gradient patterns of acceptability are most plausibly interpreted as involving soft constraints—non-categorical grammatical constraints that encode the conventional preferences of language users.
Computer crime as a modern form of crime

The development of technology has significantly contributed to the improvement of communication, the way of doing business of humanity, the flow and availability of information via the Internet, but it also entails negative consequences in the form of abuse by committing crimes in the field of computer security. The fight against it is very difficult, and the reason for that is the constant progress, ie modification of this form of crime, where it is necessary to change the existing legislation, achieve international cooperation, invest money in equipment suitable for adequate response to computer crime. In this paper, the emphasis is placed on the very concept of computer crime, international and domestic legislation, the forms in which it most often manifests itself.
Learning from Synthetic Vehicles

In this paper, we release the Simulated Articulated VEhicles Dataset (SAVED) which contains images of synthetic vehicles with moveable vehicle parts. SAVED consists of images that are much more relevant for vehicle-related pattern-recognition tasks than other popular pretraining datasets such as ImageNet. Compared to a model initialized with ImageNet weights, we show that a model pretrained using SAVED leads to much better performance when recognizing vehicle parts and orientation directly from an image. We also find that a multi-task pretraining approach using fine-grained geometric signals available in SAVED leads to significant improvements in performance. By pretraining on SAVED instead of ImageNet, we reduce the error rate of one of the state of the art vehicle orientation estimators by 51.2% when tested on real images. We release SAVED and instructions on its usage here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://taesoo-kim.github.io/
City Scene Anomaly Detection: Survey

Computer vision has evolved over time and is playing a vital role in many applications reducing the need for human supervision. In today's world, video cameras are installed at every corner producing huge amounts of data every second. This data has the potential to produce valuable insights which can produce beneficiary results for researchers as well as the society. Today artificial intelligence is redefining the lives of millions of people. This paper presents works which are done in the field of detecting anomalies. We have revisited several of the researches and have summarized their works. We have also implemented the code and present our work in this paper.
Optimizing sentiment strength detection for classification for airline opinion texts

Short text is rapidly expanding, and it is critical to understand why. Unlike traditional documents, short text is short and sparse, making machine learning and text mining algorithms difficult to apply. The process of identifying and categorizing ideas expressed in a text into positive, negative, and neutral polarities is referred to as sentiment analysis. The selection of features for machine learning is critical. The evaluation of search engines is a critical topic in the field of information retrieval. Researchers can determine whether their new algorithms add value or should be scrapped by effectively evaluating their algorithms. Organizations create cutting-edge methods for assessing their performance. Social tags have become an important source of information in web 2.0 for making personalized recommendations by identifying user interests and preferences. The purpose of this research is to systematically represent the actual topics of tags, the content of items, and user interest in specific topics via popular tags based on the content of the tags, items, and topics. The goal of this paper is to propose an innovative method for identifying user topic interests using popular tags. This paper proposes a method for extracting the main features of short text at various granularity levels. One aspect of how the content of items is represented in this paper is through the use of popular tags, but the representation of user interests is also discussed. By analysing a small dataset of short text classifications, we were able to compare our proposed method to a state-of-the-art baseline. We reduced the classification error by 20% using two different analysis methods, while the other method reduced it by 16.68%. The original tags of users are used to generate topic interests, from which we identify popular tags. The popular tags are then used to generate topic interests using an autoencoder approach.
Clustering-Assisted Regional Spatio-Temporal Sequence Pattern Mining in Crime Database

With the recent advances in IoT and other smart devices, an explosive amount of data, both spatially and temporally significant are generated. Discovering interesting or useful patterns from these spatiotemporal data is referred to as spatiotemporal data mining. These patterns could be unordered, totally ordered or partially ordered based on the temporal ordering. This work focusses on the totally ordered patterns or sequential patterns from spatiotemporal event database. Spatiotemporal event sequence miner finds sequence of events that overlaps spatially and temporally. Traditional approaches discover patterns that are frequent in the entire dataset. In this work a clustering-assisted approach to find regionally or locally frequent spatiotemporal pattern is proposed. The proposed Clustering assisted Regional Spatiotemporal Event Sequence (CReST) mining approach overcomes the bias caused by uneven distribution of spatiotemporal events while mining patterns. Chicago crime dataset is used for evaluating the proposed approach with traditional sequence mining algorithm.
Fine-Grained Activity Recognition Based on Features of Action Subsegments and Incremental Broad Learning

Human activity recognition using MEMS on mobile devices has become one of the most compelling solutions owing to the miniaturization of sensors. A crucial challenge is to recognize precisely activities when they are changing. Sliding window is a type of common methods. However, the interference of historical data in the sliding window is harmful to insight into changing of actions or uncommon behaviors. This paper proposes a fine-grained activity recognition method and designs a corresponding system farer. It employs features of action subsegments and incremental broad learning to precisely distinguish the alterations of actions and abnormal movements. Firstly, farer achieves the accurate segmentation of activities as data preprocessing. A neighborhood extreme value method (NEV) is adopted to avoid the intervention of peaks and valleys of data. Secondly, the current action is partitioned to fine-grained subsegments to elaborately abstract subtle features. We propose a feature extraction technique based on adjacent difference (FETAD), and furthermore reduce its resulting dimension through the complete two-dimensional principal component analysis (C2DPCA). Finally, broad learning theory is employed to construct the activity recognition model, especially incremental learning for unusual behaviors. Extensive experiments demonstrate that farer could accurately recognize activities when they abruptly change, and its performance is considerable stability. Meanwhile, it can quickly establish a valid incremental model that only needs a short sampling time for special activities. The overall accuracy of farer is 97.91% with 90.14% for changed activities, which is far superior to the current mainstream methods.
Vibe check

Social Resonance is a common socio-behavioral phenomenon in which users are more influenced by opinions that have similar vibes. That is, opinions from two different groups of users can mutually reinforce (or resonate with) each other to have an even stronger impact on the user. In this paper, we explore the powerful social resonance effect between social connections and other users in an eCommerce platform to improve recommendation. Specifically, we first formulate an item-aware user influence network that connects users who rate the same item. With the social network and item-aware user influence network, a novel graph-based mutual learning framework is proposed, which captures the resonance influence from both user local correlations and global connections. We then fuse these influence paths to predict the resonance-enhanced user preference towards items. Experiments on public benchmarks show the proposed approach outperforms state-of-the-art social recommendation methods.
Fast Obstacle Detection Platform Based on Yolo

Due to its energy saving and environmental protection, large passenger capacity, high speed, and high punctuality, subway transportation has received unanimous praise from the public. However, train operation accidents happen from time to time. In order to reduce the accident rate in train operation, people have put forward higher requirements for the safe and stable operation of trains. Due to the development of unmanned driving technology, a vision-based active obstacle detection system is urgently needed for the operation of the subway. In this experiment, the YOLO network (single step detection network) was used. This platform can effectively detect target obstacles in the image. The mAP value reaches 95.47%, the pedestrian accuracy rate reaches 94.75%, and the train accuracy rate reaches 96.19%. At the same time, how to identify the target in a specific area and shorten the time of image recognition is also the direction of future research.