Reviewerly/exHarmony huggingface dataset:
Keynote Speech VIII: The Role of Modelling in Business Software Development: Case Study of Teaching and Industrial Practice in Zrenjanin, Serbia

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. This paper describes the role of modelling and visualization in artefacts of software development, in the context of diversity in modelling methods, languages and tools, different software development processes and architectures. It particularly addresses issues in reverse engineering, reengineering and modelling with agile software development, which leads to the need for adaptable and adaptive modelling. This paper contributes with the description of the previously presented methodology of integrated modelling approaches in business software development and empirical research with two case studies. First case study shows experiences of modelling business-oriented software in educational process at University of Novi Sad, Technical faculty “Mihajlo Pupin”, Zrenjanin, Serbia, while second case study analyses experiences of IT companies in Zrenjanin region, Serbia. Conclusion of the paper is that educational process in teaching modelling gives mental models to students to use them in industrial practice. Agile software development with constant changes in software products leave companies out of modelling or minimize their use. Adaptive and adaptable modelling and visualization is needed in industrial practice, since it brings lower software development costs.
Advances In The Use Of Neural Networks To Process And Analyze Medical Image

Images are the largest source of data, especially in the medical field, but it is sometimes difficult to detect all the information in them.  Image processing, therefore, plays a major role in helping doctors and specialists detect lesions and diseases. With technological development, researchers began using artificial intelligence with sophisticated neurotic networks to process and analyze images. This has contributed to more accurate information, and there have been radical changes in the way doctors use x-rays and organic images. This research discussed how neural networks work and how they are used in image processing, and the most important technologies newly used in this field.
Online Learning of Computer Science to Schoolchildren Using Interactive Educational Posters

This article deals with questions of organization online schoolchildren training using interactive posters as multidimensional didactic tools for a computer science teacher. The concept of “interactive posters” discusses and shows their important role for activating and managing the activities of schoolchildren in full-time and remote forms of interaction. We are considering the design and development of interactive posters on the web platform with the specifics of training in informatics and overcoming the problems of modern schoolchildren related to clip thinking.
Outcomes of Organizational Trust: A Multi-Level Perspective and Meta-Analytic Examination

Recognizing that organizational trust is inherently multilevel, scholars have adopted a level-of-analysis perspective in studying this phenomenon. However, research has predominantly focused on cross-level effects of team-level factors, thereby neglecting factors residing at higher levels of analysis (e.g., societal) and alternative models integrating levels (e.g., multilevel and compositional models). Consistent with these limitations, scholars have converged around the need to address three unanswered levels-of-analysis questions: 1) Are relationships of trust with other variables homologous across levels of analysis? 2) How does societal-level culture shape lower-level relationships of trust with other variables? 3) How does trust consensus help clarify the way trust operates at the unit level? This paper aims to advance understanding of these issues through a meta-analytic examination. In line with our multilevel theorizing on homology, we find that trust-outcome relationships are different in magnitude across levels. Contrary to our situational strength prediction, our cross-level findings reveal that Cultural Tightness strengthens individual-level trust-outcome relationships. Finally, our compositional findings demonstrate that trust consensus promotes unit outcomes both directly and by moderating the impact of trust magnitude. Our study advances a multilevel understanding of trust and elaborates on current theories by revealing discontinuities in how trust operates and manifests across levels.
Analysis of Variants of the Even-Mansour scheme

There have been many papers on minimalism of cryptography. Secure minimal block cipher is one of these topics and Even and Mansour suggested a simple block cipher. The Even-Mansour scheme is a block cipher with one permutation and two whitening keys. Studying related to the Even-Mansour scheme gives great insight into the security and design of block cipher. There have been suggested many trials to analyze the security of the Even-Mansour scheme and variants of the Even-Mansour scheme. We present a new variant of the Even-Mansour scheme and introduce a variant of the Even-Mansour scheme. We focus on the security of these variants of the Even-Mansour scheme and present variation of the security according to key size. We prove the security of a variant of the Even-Mansour scheme and show that a generalized Even-Mansour scheme is not proper for a minimal block cipher.
Exploring the Latent Space

It is easy to get caught up in the mystique of artificial intelligence and machine learning when you view the results of an autoencoder or generative adversarial network. The results of these deep learning systems can appear magical and even show signs of actual intelligence. Unfortunately, the truth is far different and even challenges our perception of intelligence.
Secure and efficient retrieval of video file using bloom filter and hybrid encryption algorithms

Video files are the important source for the big data. Storing, managing and processing the video files are becoming more challenging day by day. Securing and retrieving the video files from the cloud storage has become one of the hot research topic as the vulnerability remains high. Usually, the confidential video files such as CCTV monitoring, OTT videos are transferred to the cloud storage using an insecure communication network. To protect the video file, existing technique uses a symmetric or single key encryption, which uses only one key to perform encryption and decryption. Symmetric key encryption reduces the work load key management and computation time. However, symmetric algorithms are vulnerable to various attacks as they use only single key to perform encryption and decryption. Likewise, existing video file encryption are inefficient to retrieve a particular block of data. In this research work, we create a hybrid model, which integrates the symmetric and asymmetric key encryption to tighten the security. Likewise, to retrieve a particular block of video file from the cloud storage, a bloom filter based index structure is maintained by the cloud service provider. The experimental result shows that the proposed model reduces the computation time and improves the security of the multimedia files stored in the cloud.
Pemanfaatan ANN untuk Prediksi Penjualan Online Industri Rumahan selama Pandemi Covid-19

Research has been conducted on the use of ANN for predicting online sales of the home industry during the Covid-19 pandemic. The purpose of this study is to train and test neural networks with the backpropagation method to obtain accurate forecasting results. The data were obtained by providing a list of questions to the home industry. The data obtained is separated into 2 parts, the first part is used to train the artificial neural network, the second part is used to test the performance of the artificial neural network. From the training that has been carried out, the success of finding goals according to the predetermined error tolerance value is 0.02 at the 33 epoch, input layer is 5 neurons, the hidden layer is 3 neurons, error tolerance is 0.025, learning rate is equal to 0.1. From the test, we managed to find a goal according to the predetermined error tolerance value, which is 0.2 at the 100 epoch, the input layer is 5 neurons, the hidden layer is 4 neurons, error tolerance is 0.2, the learning rate is equal to 0.1. The conclusion is that the artificial neural network can perform training and testing well, so as to produce precise and accurate predictions, the results of these predictions can be taken into consideration for making a decision.
A Comparative Analysis for Binary Search Operators used in Artificial Bee Colony

Metaheuristic optimization algorithms are developed to find the best or near-best solutions within a reasonable time frame utilizing various neighbourhood functions (i.e., operators). Variety of studies have been proposed for structural modifications on metaheuristic approaches or utilization of various operators. Some of these operators help fast convergence at the beginning but lose efficiency relatively or completely towards the end or vice versa. The individual and collective behaviors of operators in the search space plays crucial role in producing fruitful solutions to approximate the optimum and to devise useful adaptive selection schemes in the cases of using multiple operators. To the best of our knowledge, collective behaviour of binary operators has not been analysed comprehensively. In this study, the characteristics and collective behaviour of operators that can work on discrete decision variables within an artificial bee colony are investigated over solving OneMax and SUKP problems utilizing 9 different operators. The results conclude that disABC, GBABC and twoOptABC operators are more effective in solving OneMax problems, while GBABC and twoOptABC are more effective (especially towards end) in the SUKP problems.
The role of group composition for cognitive collaborative foresight processes

Corporate foresight is said to be a promising approach to reduce or handle uncertainty arising from fast changing, complex environments. The added-values mentioned in literature are among others, that it prepares for the future by challenging dominant mental models that decision-makers have adopted from the past and helping them to overcome the inertia inherent in their beliefs. The literature review shows that collective cognition – and in this regard group composition – plays an important role in how far proposed qualities of cognitive foresight can be met and cognitive biases (e.g. group polarization phenomenon, confirmation bias) can be overcome. This research investigates how group composition (in terms of homogeneity versus heterogeneity) affect cognitive foresight processes and how cognitive pitfalls are likely to be overcome. Based on an integrated literature review, we propose a conceptual framework of a collaborative foresight that benefits from inter-organizational diversity and offer suggestions to guide future empirical and theoretical research.
Gray Image and Color Image Security Using RSA Algorithm

For image security and protection we utilize cryptographic. In cryptographic we encrypt the information. There are many symmetric and asymmetric encryption techniques, which are used for encryption and decryption purpose. Symmetric encryption strategy work on regular key (same key utilized for encryption and decryption reason), though asymmetric work on with one public key and private key.  Public key utilized for encryption and private key for decryption. The security of the stowing away is measure utilizing the MSE, PSNR, Cross-Correlation and Entropy boundaries. For better stowing away get the low MSE and high PSNR esteems. So to make the quick reaction we need calculation which takes less time and give reaction quick. Henceforth likewise analyze the time reaction of purposed calculations to improve security and quick response.
PENERAPAN METODE FONIK DALAM MENINGKATKAN KEMAMPUAN MEMBACA PERMULAAN PADA ANAK TUNAGRAHITA RINGAN KELAS 5 SD DI SLB B-C NIKE ARDILLA YPWN

Reading is a basic asset for students in learning at school. Children with intellectual disabilities have difficulty in understanding things that are abstract and tend to be difficult to remember, resulting in mild mental retardation experience obstacles in recognizing letters so that mental retardation experiences obstacles in reading. The purpose of this study was to determine whether there was an influence on the use of phonic methods in improving the ability to read the beginning of class V in SLB B-C Nike Ardilla YPWN Bandung. This study uses an experimental method of Single Subject Research (SSR) research design with A-B-A pattern design. Data collection techniques using the test. Analysis of the data used is the analysis of data in conditions and between conditions. The subjects of this study were two children. In the first subject, TA, initial reading ability at baseline-1 (A1) rises, interventions (B) rises, and baseline-2 (A2) rises. This can be seen from the overlapping conditions on subject one which amounted to 16.6%. In the second subject, AG, initial reading ability at baseline-1 (A1) ascends, interventions (B) rises, and baseline-2 (A2) rises. This can be seen from the overlap condition in the second subject which is 100%. Based on the results of this study it can be concluded that the application of phonic methods can improve the ability to begin reading in mild retarded children.
Developing an Android-Based City Tour App using Evolutionary Algorithm

Traveling around a city and making transit in certain areas is called a city tour. Furthermore, determining the optimal city tour route can be considered as a traveling salesman problem. There are many kinds of algorithms to solve this, one of which is the Genetic Algorithm (GA). In developing the City Tour application, a platform is needed to be taken to various places anywhere and anytime. Finally, we developed an application that runs on mobile devices. This application is built on the Android platform so that its use can be more efficient. Furthermore, it can be concluded that the GA applied to the Android-based City Tour Application is reliable to determine city tour routes; this is evidenced by comparing GA with the brute force method, where GA provides optimum results with less running time.
Capienza delle forme vascolari, da un metodo open source all’uso di modelli regressivi: il caso dell’insediamento protostorico del Monte Cimino

The volume of ceramic vessels provides several information about their use. However, due to the fragmentation of pottery coming from settlement sites, data concerning the volume are rarely published. The first goal of this paper is presenting a new method to calculate the volume starting from the archaeological drawings. The dataset could be extended with a predictive regression analysis. The sample analyzed involves cups and bowls found on the top of Monte Cimino (Viterbo-Italy), a settlement and cult site dated to the Final Bronze Age (ca. 1150-950/925 BC).
PROFESI AKUNTAN : AKAHKAH HILANG DI ERA DIGITAL 4.0?

The results of research from McKinsey in 2016 that the impact of digital technology the 4.0 industrial revolution in the next five (5) years there will be 52.6 million types of jobs that will disappear from the earth. And one of the professions is the accounting profession. And the fact is some of the accounting profession's work has begun to be taken away, replaced by technology. This shows automatic journaling, sorftware in the field of accounting has been sold in the community, so that simple accounting jobs do not need an accountant with a college degree. This situation raises the question whether the entire accounting profession is responsible? This study used a literature approach. Several viewpoints and research results state that the accounting profession will be replaced by robots, especially accountants and auditors. However, this profession is still relevant to the presence of the 4.0 industrial revolution era if accountants can make adjustments to technology, especially the Accounting Information System, must be able to answer the challenges in the era of the industrial revolution 4.0. especially accountants must be able to prepare themselves are able to own and improve their competence, so the profession is not taken over by other professions.
ABNORMAL EVENT SUMMARIZATION IN VIDEO SURVEILLANCE USING HIERARCHICAL RECURRENT NEURAL NETWORK

Roadways play a vital role among the various transportation modes in the world for people. Even in developing countries, death occurs every four minutes cause of road accidents which is a deep concern to humanity. Intelligent Transportation system, Vehicle behaviour analysis, road accident detection, and event summarization are the great interest fields of research. From the surveillance videos, providing the summarization of abnormal events is quite complex. To solve this issue, a technique called conceptual video summarization is presented which enables the speed of accident events visualizing from the videos in a stack. In this proposal, the video summarization is carried out using a Hierarchical-Recurrent Neural Network which is used to summarize the abnormal events in the video surveillance. The primary aim of the suggested video resuming method is to summarise and display the anomalous event portions of the video to the user. This work exploits the dependency of long temporal among the frames and the computations are less when compared to the traditional Recurrent Neural Network. The experimental results show that the proposed system provides higher accuracy when compared with state-of-the-art techniques.
Where does gold come from?

Many elements are made in stars like our sun. But new evidence suggests gold's origins could be far more ancient. Sapphire Lally investigates
Application of Gesture Recognition Fusion Algorithm Based on Double LeapMotion in Hand Function Rehabilitation in Large Space

With the development of human-computer interaction technology, due to the advantages of virtual interaction and the quantification of motion capture data, gesture recognition controllers such as LeapMotion have been introduced into hand function rehabilitation training. However, when used individually, its measuring angle and measuring range are limited and cannot be applied in a large space. In this paper, the dual LeapMotion combination is used to unify the data in the two coordinate systems into the same coordinate system, thereby increasing the acquisition space range and space field of view. Through coordinate system conversion, the converted data is consistent with the actual test action. This method can be extended to multiple LeapMotion combinations.
Soft2Soft: Toolkit for the Digital Recognition of Textile Gestures

This work in progress presents the toolkit Soft2Soft, for the digital recognition of textile gestures. In this context, the term gesture means a non-verbal way of human-computer interaction (HCI), through the hands, using textile materialities. The first section presents a background of projects that involve HCI with textile elements, which have inspired and informed our work. The second section details the toolkit that provides gesture recognition over wearable textiles (through actions such as button-up, sticking hook and loop pieces, sliding a zipper) and non-wearable textiles (e.g., going through the fabric with a needle, caressing the textile, squeezing, or pinching the fabric). Here, we emphasize calling the interaction through (with) the textile materialities as a gesture, meaning that it intends to express a feeling, an idea, or a thought, not as just a simple, functional action. Finally, we present and discuss some preliminary uses of the toolkit and potential applications that Soft2Soft will allow.
Exploring the Multimedia Data Compression Attributes

Multimedia data such as image, data, video, &audio data need much transmission bandwidth & storage capacity. In spite of fast development in digital communication framework presentation, request for “data-transmission bandwidth &data storage capacity continues to outstrip the abilities of accessiblemethods. The latestdevelopment of “multimedia-based web applications” has not only essential for furtherproficientpaths to encode images &signals but have made compression of such signals central to communication &storagetechnology. The data compression (DC)objective is to signify any media as accurately as probable utilizing the least number of bits. This manuscript discovers attributes for image, audio, data, &video compression (VC).
Unifying Models for Belief and Syllogistic Reasoning

Author(s): Brand, Daniel; Riesterer, Nicolas Oliver; Ragni, Marco | Abstract: Judging if a conclusion follows logically from a given set of premises can depend much more on the believability than on the logical validity of the conclusion. This so-called belief bias effect has been replicated repeatedly for many decades now. An interesting observation is, however, that process models for deductive reasoning and models for the belief bias have not much of an overlap - they have largely been developed independently. Models for the belief bias often just implement first order logic for the reasoning part, thereby neglecting a whole research field. This paper aims to change that by presenting a first attempt at substituting the first order logic components of two models for belief, selective scrutiny and misinterpreted necessity, with two state of the art approaches for modeling human syllogistic reasoning, mReasoner and PHM. In addition, we propose an approach for extending the traditionally dichotomous predictions to numerical rating scales thereby enabling more detailed analysis. Evaluating the models on a dataset published with a recent meta-analysis on the belief bias effect, we demonstrate the general success of the augmented models and discuss the implication of our extensions in terms of the limitations of the current focus of research as well as the potential for future investigation of human reasoning.
Visual Attention in Human−Machine Interaction

Humans are incapable of attending to everything at the same time. The serial nature of focused attention limits the information intake capacity of the perceptual system. This thesis deals with the measurement and modelling of visual attention distribution. It is examined whether measures of visual attention are predictive of task performance. The first chapter introduces the main topic of this thesis: the complex nature of modern technological systems, which feature many information sources that have to be monitored. Many psychological constructs have been proposed in the human factors literature that have alleged criterion validity for task performance. Here, task performance is regarded as the human’s ability to e.g., take over control of an automated system in potential critical situations. Contrary to the speculative nature of some of the Human- Factors constructs, this thesis sets out to capture performance in terms of objective measures of visual attention. Wickens’s (2008) Salience, Effort, Expectancy, Value (SEEV) model is introduced and discussed. This model is utilized for interpreting the eye-tracking results. Finally, a rationale for the topics in the thesis is provided. Chapters 2 through 4 of this thesis discuss and elaborate on Senders’s (1983) research in detail, by means of replication research and an extensive tutorial on his mathematical models. These chapters provide an empirical underpinning and conceptual understanding of the concept of visual attention. Chapters 5 through 8 discuss visual attention in light of Air Traffic Control (ATC) and automated driving, and are regarded as suitable cases for attention distribution measurement and task performance prediction. Chapter 9 investigates task performance and visual attention in a psychometric task: Inspection Time, which provides a good testbed for operationalizing the effect of attention on task performance. Chapter 10 concludes with a discussion on the topics in this thesis...