Reviewerly/exHarmony huggingface dataset:
Transformer-Based Zero-Shot Detection via Contrastive Learning

Zero-Shot Detection (ZSD) is a challenging computer vision problem that enables simultaneous classification and localization of previously unseen objects via auxiliary information. Most of the existing methods learn a biased visual-semantic mapping function, which prefers predicting seen classes during testing, and they only focus on region of interest and ignore contextual information in an image. To tackle these problems, we propose a novel framework for ZSD named Transformer-based Zero-Shot Detection via Contrastive Learning (TZSDC). The proposed TZSDC contains four components: transformer-based backbone, Foreground-Background (FB) separation module, Instance-Instance Contrastive Learning (IICL) module, and Knowledge-Transfer (KT) module. The transformer backbone encodes long-range contextual information with less inductive bias. The FB module separates foreground and background by scoring objectness from images. The IICL module optimizes the visual structure in embedding space to make it more discriminative and the KT module transfers knowledge from seen classes to unseen classes via category similarity. Benefiting from these modules, the accurate alignment between the contextual visual features and semantic features can be achieved. Experiments on MSCOCO well validate the effectiveness of the proposed method for ZSD and generalized ZSD.
Ensemble Semi-supervised Machine Learning Algorithm for Classifying Complaint Tweets

Nowadays, social media platforms, specifically Twitter, are considered as the convenient and fastest mode of sharing all the urban issues. Extracting the grievance tweets for further analysis to get the better idea of the situation and provide timely help really can play critical role for local governance. High velocity of the tweets makes the manual identification of the relevant complaint tweets a strenuous task. Existing studies experimented with supervised learning algorithms to automate the task but having requisite of large amount of annotated data which is a very exhausting process. So in this paper, we propose the ensemble of co-training and self-training semi-supervised algorithms which conquer the unavailability of huge quantity of labeled data and precisely segregate between complaint and non-complaint tweets. We have experimented with various combinations of classifier algorithms and features dataset of 1000 tweets to validate out approaches. The result of F1 score as 88% and accuracy as 89% clearly indicate that our approach is quite promising in identifying the compliant data.
Automatic Image Caption Generation Using CNN, RNN and LSTM

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Safeguarding Future Autonomous Traffic by Infrastructure based on Multi Radar Sensor Systems

"Due to its robust operation and high performance during bad weather conditions and overnight as well as the ability of using the Doppler Effect to measure directly the velocity of objects, the radar sensor is used in many application fields. Especially in automotive many radar sensors are used for the perception of the environment to increase the safety of the traffic. To increase the security level especially for vulnerable road users (VRU’s) like pedestrians or cyclists, radar sensors are used in driver assistance systems. Radar sensors are also used in the infrastructure, e.g. a commercial application is the detection of cars and pedestrians to manage traffic lights. Furthermore, radar sensors installed in the infrastructure are used in research projects for safeguarding future autonomous traffic. The object recognition and accuracy of radar-based sensing in the infrastructure can be increased by cooperating radar systems, which consist out of several sensors. This paper focus on the data fusion method of two radar sensors to increase the performance of detection and localization. For data fusion the high level cluster data of the two radar sensors are used as input data in a neuronal net (NN) structure. The results are compared to the localization obtained by using only a single radar sensor operating with an ordinary tracking algorithm. First, different models for chosen region of interests (ROI) and operating mode of cooperative sensors are developed and the data structure is discussed. In addition, the data are preprocessed with a coordinate transformation and time synchronization for both sensors, as well as the noise filtering to reduce the amount of clusters for the algorithm. Furthermore, three NN structures (CNN, DNN and LSTM) for static + dynamic objects and only dynamic objects are created, trained and discussed. Also, based on the results further improvements for the NN performance will be discussed."
Reboot the (Legal) System: Can Artificial Intelligence Make Legislation Economically Rational, Transparent, Participatory, and Representative?

Legislation’s replacement of Common Law as the dominant means of lawmaking has coincided with developments in artificial intelligence, causing legal scholars to address the relationship between legislation and artificial intelligence. Mises’s Impossibility Thesis argues that economic calculation under socialism is impossible, precluding rational allocation of scarce resources and property rights. Subsequent Austrian scholars extended this thesis to legislation. Legal scholarship optimistic as to the effects artificial intelligence can have for drafting legislation and legislative policy claims that such engenders better and more consistent laws, greater government transparency, increased public participation in lawmaking processes, and more responsive government. These arguments are analogous to Computational Socialist arguments. Can artificial intelligence make legislation economically rational? This Paper answers in the negative. Legislation, being socialistic, is antithetical to private property; it therefore inherently distorts or destroys competitive bidding processes between private property owners, thereby distorting or destroying market prices. Consequently, artificial intelligence used for legislation would suffer from a “garbage-in-garbage-out” problem – i.e., artificial intelligence would not have access to market prices, precluding rational economic calculation. This problem cannot be solved via raw computing power, nor software sophistication. Thus, and especially when viewed with supplemental public choice considerations, legislation generated by artificial intelligence would be untransparent, unparticipatory, and unrepresentative of constituents. To create economically rational law in a society facing ever increasingly faster rates of change due to technological progress, centralized legislation and regulation through public law must be replaced with decentralized, Common Law-type structures based upon private law and private property.
VIRTUAL ACADEMIC MOBILITY IN HIGHER EDUCATION

The pandemic has intensified the processes of digitalization in academic culture, which was manifested in the form of intensive development of digital platforms in education and in the request for virtual academic mobility (VAM). The advantages of academic mobility (AM) include accessibility of the scientific and educational environment of other universities since the medieval universities and since the beginning of the Bologna process, and VAM makes educational programs accessible from anywhere in the world, including asynchronous interaction. Due to the pandemic, academic exchange educational programs have switched to the VAM format, according to a survey conducted by the authors of the article. The participants of the academic communication named financial and language problems, socio-cultural differences and difficulties with IT-support among the main difficulties of the AM. These factors make the national AM in demand, which will increase the interest of graduates in the regions as a place of work. It is necessary to create a digital platform for VAM, to solve communication, organizational, managerial and technical problems. It is necessary to take into account the properties of virtual reality, the features of the educational process and the culture of academic communication between participants when designing a platform.
ESTABLISHMENT OF THE STUDENT LIBRARY OF THE KYIV THEOLOGICAL SEMINAR AND ITS FILLING IN THE LAST THIRD OF THE XIX – EARLY XX CENTURY

The creation of a student library in the Kyiv Theological Seminary has been studied. It was found that the main reason for this was a chronic lack of educational literature. The exact year of foundation of the book depository has been found out. The peculiarities of the fundraising process are described, and the main emphasis in the research is on filling the student library in the first years of its existence. Attention is drawn to the fact that a separate room in the seminary buildings was initially allocated for her. It is proved that the student library had to perform the educational function. It was found that the total amount of contributions of pupils in the 1870–1880’s was about 200 rubles per year, and at the turn of the XIX–XX centuries – ranged between 300 and 400 rubles for a year. It was investigated that the amount of donations was several tens of kopecks per person. It is determined that they met with all students without exception, first twice and then three times a year. It was found that at the end of the XIX century the student library contained more than 1 500 titles of books in more than 3 500 volumes.
Survey on load balancing techniques and resource scheduling in cloud computing

Load balancing is the double conditional service that improves performance and resource efficiency in computation. Load balancing techniques provide solutions for effective utilization of resources, improving performance, and reducing power utilization. Various load balancing techniques are proposed earlier. Traditional load balancing techniques do not support cloud technology for its dynamic behaviour. Cloud computing is highly elastic in nature, Load balancing is data intensive. The use of a machine learning approach to develop an efficient load-balancing algorithm is discussed in this paper. An efficient load balancing algorithm will reduce power consumption by utilizing minimum resources. This paper provides a review of load balancing techniques completely. The advantages and disadvantages of existing methods are discussed by addressing the challenges to establish a successful and efficient load balancing algorithm. This paper addresses the new insights towards load balancing in cloud computing.
STUDENTS’ ACQUIREMENT OF KNOWLEDGE ABOUT PROFESSIONAL CULTURE AS ONE OF THE PEDAGOGICAL CONDITIONS OF THE FORMATION OF PROFESSIONAL CULTURE OF FUTURE BACHELORS OF BANKING

The article is devoted to the analysis of pedagogical conditions of formation of professional culture of future bachelors of banking, namely pedagogical condition of mastering by students of knowledge about professional culture by directing the content of disciplines of humanitarian and professional training on involvement in values, ethical norms and rules of professional activity. The article describes the concepts of “condition” and “pedagogical conditions”, which are one of the main directions of professional culture. The pedagogical conditions that contribute to the formation of the professional culture of future bachelors of banking are highlighted. It is outlined that the pedagogical condition of students' masterying of knowledge about professional culture is realized through the inclusion in the content of normative and elective disciplines (educational components), topics and educational issues that reveal the content of the phenomenon of “professional culture”. It is revealed that the topics of lectures and practical classes can be expanded and supplemented with material related to the norms and rules of professional ethics, skills and abilities of professional communication, ways of creative selfrealization in professional activities, professional growth and moral development.
Blockchain-based IoT security in healthcare

The Internet of Things is a network that contains technologies, software, and sensors that are interconnected to facilitate communication and exchange of data with other systems using the Internet and without human intervention. Blockchain is a peerto-peer network that records information in a way that cannot be changed or hacked. It is a safe way for individuals to interact with each other without the need for an intermediary or a third party. The use of Blockchain has increased the productivity of supply chains and has had a significant positive role in distribution, payment and warehousing, as well as improving services provided to patients in healthcare. Recently, IoT technology has been combined with Blockchain technology, and this has resulted in patient data being kept confidential and sensitive. Helping doctors to provide different services to patients in a timely manner. also has increased the traceability of supply chains and also led to increased verification in them and largely eliminated fraud and forgery such as bribery, forged checks, money laundering and many more, In this paper, present an overview of the Internet of Things and Blockchain, also discusses the integration of Blockchain and IoT technologies in healthcare, and finally presents some challenges in the use of Blockchain and IoT technology in healthcare.
Design by Hand in a Digital Environment. Drawing Storytelling and ICT Development

The digital world is all around us, due to the ICT development. The current news, the academic and escapist literature, management, leisure and enjoyment are all immersed in a digital environment. Simultaneously, different forms of expression stay and live together, in which, the word, direct hearing, sight, hand gesture and hug can be observed. In a digital world of million data, the sounds and images, the silence and the slowness are also valuable. The fast food and the traffic calming coexist. Appreciating the information, communication and expression media, especially drawing, allows integrating each individual world in a collective environment.
An innovative cycle-based learning approach to teaching with analog sandbox models

Scaled analog modeling (“sandbox modeling”) allows deformational processes, such as the development of a mountain belt, to be observed in real time in a classroom setting. However, the actual learning gains from exposure to sandbox modeling in geology courses in higher education settings have not been explicitly studied. We begin to investigate the possible effects of incorporating a sandbox modeling activity on geologic understanding in an upper-level tectonics class. The designed activity utilized a cycle-based learning approach, where the 11 participating students predicted outcomes of different deformation experiments and then evaluated and revised their predictions in the light of their experimental observations. Scored predictive sketches and a spatial visualization test administered before and after the sandbox activity demonstrate improvements in geological understanding of deformation, the influence of different mechanical properties on deformation style, and penetrative thinking skill. The observed gains were particularly marked for students who had poorly developed penetrative thinking skills prior to the activity. These results indicate that the use of sandbox models in the classroom may have a measurable effect on penetrative thinking skills and geologic understanding, particularly in students with less expertise. However, further study is required to test if these effects can be reproduced, and shown to be statistically significant, in larger groups of students.
A Study on Landscape VR Design of Special Neighborhood Based on Traditional Culture

The characteristic neighborhoods with traditional culture fully reflect the city’s history and culture while carrying urban life. The essence of the regional culture is transformed into the unique symbolic elements of the city. The focuses of landscape renovation design of characteristic neighborhoods are to refine regional cultural symbols, maintain the spirit of neighborhood places, carry forward historical and traditional cultural characteristics, and enhance people’s perception of traditional culture through scientific and systematic organization and planning. This paper fuses virtual objects with real entities. This technology is aimed at providing a brand-new transformation method for construction personnel in the digital transformation of IoTs. From the perspective of the construction site application of the entire landscape design, in the process of continuous development and testing of the software, it provides a large key technology research, which provides the key for the actual construction use requirements and related technical requirements of virtual reality technology. This paper mainly analyzes the main application of virtual reality and IOTs in the digital transformation of IoTs.
An observer cascade for velocity and multiple line estimation

Previous incremental estimation methods consider estimating a single line, requiring as many observers as the number of lines to be mapped. This leads to the need for having at least 4N state variables, with N being the number of lines. This paper presents the first approach for multi-line incremental estimation. Since lines are common in structured environments, we aim to exploit that structure to reduce the state space. The modeling of structured environments proposed in this paper reduces the state space to 3N + 3 and is also less susceptible to singular configurations. An assumption the previous methods make is that the camera velocity is available at all times. However, the velocity is usually retrieved from odometry, which is noisy. With this in mind, we propose coupling the camera with an Inertial Measurement Unit (IMU) and an observer cascade. A first observer retrieves the scale of the linear velocity and a second observer for the lines mapping. The stability of the entire system is analyzed. The cascade is shown to be asymptotically stable and shown to converge in experiments with simulated data.
Sinergisitas Komunikasi Antarbudaya dalam Pembelajaran Bahasa Inggris di Tingkat Universitas

Nowadays, English lesson becomes important subject that has been taught for all educational sectors. Learning English was not only focuses in improving four essential skills (speaking skill, listening skill, reading skill, and writing skills) but also it was needed to understand the cultural aspects. Regarding the phenomenon, this article described the synergisity of intercultural communication in English teaching and learning process at University. The introduction of target language culture would be helped the students to improve the language skills. Furthermore, by discovering the language culture could minimize the students’ interference during the communication. In the English lesson, university students are expected to be able to find the cultural material which suitable with English material. English lecturer could support that activity by providing English text that is written by native writer and showed the real phenomena in those areas. Besides, table manner activity helped the students to catch the tradition of western culture about cutlery, technique and also dining etiquette. The conversation practice with native speaker could also increase the student ability in speaking skill. Thus, the students were able to respect the world’s culture through intercultural communication.
Specialized software for simulating dynamic virtual machine consolidation

For many cloud service providers, virtual machines remain the basic technology for computing virtualization. Virtual machines are used both to host application software and to implement container virtualization. Widespread use of virtual machines develops specialized software to determine the impact of model parameters on the quality of the consolidation process, which will prevent experimental research in production to evaluate new cloud data center resource management strategies. In recent years, there were many approaches in literature that offers various sets of software tools and frameworks for modeling data center processes, providing a platform and the necessary building blocks to optimize the process of consolidation of virtual machines. Models and software tools for modeling data center resource management processes are usually not exhaustive and solve a specific problem or management task. The specialized simulation software presented in the paper allows to investigate different control modes of virtual machines dynamic consolidation, provides a wide range of logging and debugging information using text and MS Excel files, such as performance indicators and workload diagrams, and allows to determine the optimal model parameters for various modes of data center operation, minimizing the number of active physical servers and reducing the number of SLA violations.
Lower Bounds for Restricted Schemes in the Two-Adaptive Bitprobe Model

In the adaptive bitprobe model answering membership queries in two bitprobes, we consider the class of restricted schemes as introduced by Kesh and Sharma [1]. In that paper, the authors showed that such restricted schemes storing subsets of size 2 require $$\varOmega (m^\frac{2}{3})$$ space. In this paper, we generalise the result to arbitrary subsets of size n, and prove that the space required for such restricted schemes will be $$\varOmega (\left( \frac{m}{n}\right) ^{1 - \frac{1}{\lfloor n / 4 \rfloor + 2}})$$ .
Routing and Navigation: Part 3

In this chapter, I continue to describe the Angular URL routing system, focusing on the most advanced features. I explain how to control route activation, how to load feature modules dynamically, and how to use multiple outlet elements in a template. Table 26-1 summarizes the chapter.
Using K-Nearest Neighbours Feature Selection for Activity Recognition

As Team 2A&B Core, we contributed a K-nearest neighbour approach for classifying ten different activities for the Bento Packing Activity Recognition Challenge. We used hand-engineered features from motion capture data. Before classifying the data, we scaled it and applied a principal component analysis. We used the K-nearest neighbour classifier to find the best feature, and we found that the best feature was one that was related to a fixed position. Our approach achieved an accuracy of 38.78% on our validation subject and 42.00% on our test subject.