Reviewerly/exHarmony huggingface dataset:
Concept Drift Handling in Information Systems: Preserving the Validity of Deployed Machine Learning Models

Predictions computed by supervised machine learning models play a crucial role in a variety of innovative applications in business and industry. Typically, value is generated as soon as these models are deployed and continuously used in information systems of an organization. However, machine learning endeavors predominantly focus on conceiving applications for static situations. In this context, the management of the models’ lifecycle to preserve their effectiveness over time in dynamic environments is still in its infancy.Therefore, this thesis starts with systematically analyzing the full lifecycle of machine learning applications from an information systems (IS) perspective—and understanding and documenting all choices that have to be made throughout this cycle. On that basis, we then perform a qualitative study via practitioner interviews to map particular challenges in the deployment phase. In this context, we identify concept drift as a particularly important challenge to overcome: Concept drift refers to changes in the environment over time which affect the behavior of a machine learning model. This can have an impact on the model’s prediction quality and its overall utility.We analyze and categorize concept drift handling approaches covering both the detection of concept drift as well as the appropriate adaptation of the model. We identify two particular research gaps: the handling of concept drift for regression tasks and the handling of concept drift for tasks where additional labels for retraining a model are hard or costly to obtain. For both areas, we develop new methods and demonstrate their effectiveness in technical experiments and real-world use cases.This thesis contributes new methods to handle concept drift, for particular difficult contexts: First, this thesis should raise researchers’ and practitioners’ awareness for the topic of changing input data over time as well as for its impact on deployed machine learning. Second, the developed and tested methods can either be implemented directly or serve as inspiration to conceive appropriate drift handling strategies within information systems. Finally, we expect that advanced concept drift handling not only technically ensures a reliable prediction quality over time, but that it will also increase trust and acceptance of machine learning-based information systems—and, thus, help to boost the impact of machine learning.
Elliptic Curve Cryptography with Reduced Cipher Bandwidth-A New Approach to Conventional ECC

Public key Cryptosystem (PKC) is ever expanding in the history of cryptography. Traditional Public Key Infrastructure (PKI) certificates assure the authenticity in the form of digital signature. Elliptic curve cryptography is an incredible and complex public key cryptosystem having wide range of practical applications in wireless environments like smartcards, tokens, and mobile phones. The attractive feature of ECC is it offers equivalent level of security as RSA/DSA with much smaller keys. This is the reason that ECC has several applications in wireless constrained environments especially in Secure Sockets Layer (SSL) handshakes to establish SSL connection.  For implementing the text encryption in ECC one plaintext point on the elliptic curve is encrypted as two cipher text points. So, the main drawbacks of ECC is that it increases the size of the cipher significantly more than RSA encryption. This paper aims at presenting innovative elliptic curve cryptosystem that reduces the cipher size equivalent   to that   of the plaintext which reduces the communication risk, storage space and power consumption.
Introduction to Ethical Hacking

In this chapter, you will learn about the five phases of ethical hacking and the different types of hacker attacks.
VISUAL OBJECT DETECTION BY COLOR, SHAPE AND DIMENSION (DETECCIÓN VISUAL DE OBJETOS POR COLOR, FORMA Y DIMENSIÓN)

Abstract 3D neural object detection by color, shape and dimension (3DOD-CSD) is a novel and powerful tool that can be used to build object detection systems in environments with uncontrolled illumination. Inspired by the global structure of the human visual system, it uses a neural network in the classification stage and determines the physical dimension of the object’s fea-tures using commercial digital cameras calibrated in stereo configuration. This permits the analysis of images of objects with the same form and color but different dimensions, such as scaled replicas or photographs of a photograph of a 3D object. With this method, a fixed distance from the camera to the object to be analyzed is not necessary - essential for a dynamic recognition system in changing conditions. The results show strong discrimination between desired and undesired objects. This system has many possible applications, including face identification and object selection in varying environments, utility pole detection, coin detection, and more. Keywords : Color, dimensional features, invariant features, neural network, stereo vision, 3D object recognition. Resumen La deteccion de objetos 3D por color, forma y dimension (3DOD-CSD) es una herramienta para construir sistemas de deteccion de objetos en entornos con iluminacion incontrolada. Inspirado en la estructura global del sistema visual humano, utiliza una red neuronal en la etapa de clasificacion y determina la dimension fisica de las caracteristicas del objeto utilizando camaras digitales comerciales calibradas en configuracion estereo. Esto permite el analisis de imagenes de objetos con la misma forma y color, pero de diferentes dimensiones, como replicas a escala o fotografias de una fotografia de un objeto 3D. Con este metodo, no es necesaria una distancia fija entre la camara y el objeto a analizar, algo esencial para un sistema de reconocimiento dinamico en condiciones cambiantes. Los resultados muestran una fuerte discriminacion entre objetos deseados y no deseados. Este sistema tiene muchas aplicaciones posibles, incluida la identificacion de rostros y la seleccion de objetos en entornos variantes, deteccion de postes, deteccion de monedas, entre otros. Palabras Clave : Color, caracteristicas dimensionales, caracteristicas invariantes, reconocimiento de objetos 3D, red neuronal, vision estereo.
Data Possession with Outsourced Data Transfer

On rapid improvement of cloud computing, large enterprises would like to update and reserve   data in the public cloud. Where business parts of organization are brought by other owner, similar data would sent to required system. In normal, computation cost of data sent to the cloud. The process for study data importance with outcome data sending. First time, novel concept, by taking utilization of DTPDP, the accompanying three security prerequisites can be satisﬁed other un-obtained information security of gained endeavour can be guaranteed the acquired information trustworthiness and protection can be guaranteed the information transferability's calculation can be redistributed to the general population cloud servers. For the security idea of DT-PDP, we give its inspiration, framework model and security model. At that point, we plan a solid DT-PDP conspire dependent on the bilinear pairings.
An Enhanced Access Control Mechanism for Mobile Cloud Computing

Cloud computing is the emerging technology where resources are available pay as you go basis. Cloud storage technology provides the large pool of storage capacity to the cloud users. Providing security to the data stored in cloud is the major concern. So, Security can be enhanced by providing access control to the authorized users. Access control gives the authorization to the users which gives the access privileges on data and other resources. Access control can be enabled in most of the computing environment such as Peer to Peer, Grid and Cloud.Access control is an important measure for the protection of information and system resources to prevent illegitimate users from getting access to protected objects and legitimate users from attempting to access the objects in ways that exceed what they are allowed. The restriction placed on access from a subject to an object is determined by the access policy. With the rapid development of cloud computing, cloud security has increasingly become a common concern and should be dealt with seriously. In this paper, an enhanced access control mechanism is proposed with hierarchical attribute-based access control method.
CHALLENGES OF TRANSFORMING EDUCATION IN THE DIGITAL AGE

In this paper is treated and studied the transformation of education in recent years, the era of digitalization and technology. Education was one of the most important and religiously discussed issues in recent year, with the apparent evolution of technology in everyday life, even more so in the last year, when Covid-19 inevitably subjected students so technology. Nowadays, digital transformation has permeated every field of life, changing the economy, society and the way policies are organized, which has influenced the preparation of students for the new digital age, challenging the traditional ways of education.Education in the digital age is a crucial issue for policymaker, because education must prepare citizens capable of the digital labour, market and form equal European citizens in values and skills for digital democracy. Technology has been embraced at different levels by different countries. Eurostat conducts systematic research and studies on technology transformation and digital youth education.This paper has analyzed Eurostat data, the impact of the pandemic on changing teaching and technology, the advantages and disadvantages of education in the digital age, the role of teachers and parents in the implementation of technology and the transformation of education policies in the context of digitalization
Rice Productivity Analysis by Province Using K-Means Cluster Algorithm

Rice (Oryza sativa L.) is a very important food crop in the world after wheat and corn. It is also a staple food for most of the world's population, especially in Asia, like in Indonesia until now. In 2014 to 2018, rice productivity tended to change dynamically. In 2018, rice productivity in Indonesia was 51.92 (Ku/Ha). This research was conducted to classify rice productivity in 34 provinces in Indonesia in 2018. The data used were sourced from Statistics Indonesia. The method or approach used in this study is the K-Means cluster algorithm to classify rice productivity data by province in 2018. The results of the research are; (1) There are 19 provinces included in cluster 0 (Medium), (2) There are 4 provinces included in cluster 1 (High), and (3) There are 11 provinces that are included in cluster 2 (Low). Based on the results of the study, it was proven that there were 4 provinces in cluster 1 (High) they are West Java, Central Java, East Java and South Sulawesi, with the highest rice productivity. Three of them were on Java Island. It shows that Java still dominates the productivity of rice plants.
Keynote Lecture: Neural circuit policies

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. A central goal of artificial intelligence is to design algorithms that are both generalisable and interpretable. We combine brain-inspired neural computation principles and scalable deep learning architectures to design compact neural controllers for task-specific compartments of a full-stack autonomous vehicle control system. We show that a single algorithm with 19 control neurons, connecting 32 encapsulated input features to outputs by 253 synapses, learns to map high-dimensional inputs into steering commands. This system shows superior generalisability, interpretability and robustness compared with orders-of-magnitude larger black-box learning systems. The obtained neural agents enable high-fidelity autonomy for task-specific parts of a complex autonomous system.
The Role of Using iPad Technology in Enhancing Students' Motivation toward Learning the English Language

This study aimed to find out the role of the iPad in enhancing students' motivation towards learning the English Language. The findings of the study showed that there were improvements in the level of student's performance during English classes. It also showed that there were positive attitudes towards using iPad in learning English from the parents' perspectives. The results also showed that the total degree of teachers’ attitudes towards the use of iPad in motivating students towards learning the English language is dramatically increasing. Based on these results, the researchers recommended the Ministry of Education, and teachers of English to adopt the iPad as an educational tool in their teaching.
Dual Security Using Image Steganography Based Matrix Partition

Recently, the mode of living became more complicated without computer systems. The techniques of camouflage information have acquired a vital role with the requirement of intensifying trade of multimedia content. Steganography is the technique that utilizes disguise in a way that prohibits unauthorized access from suspicion of the existence of confidential information exchanged during communication channels between the connected parties. In this paper, an integrated image steganographic system is designed to conceal images, messages or together where the mainly deliberate the improvement of embedding capacity through embedding text with image simultaneously. For that purpose, used matrix partition to partition the secret image then embedded each partition separately after scrambling each pixel by replacing msb instead of lsb to provide the second level of security furthermore to steganography. The simulation results clarify the better performance of the proposed algorithms.
What Led Us to the Data

The usefulness of Bibles in Natural Language Processing is wildly underrated, especially when looking at low-resource languages. The reason these texts are so useful in machine translation is because it provides parallel text in languages that would otherwise have non or very little other parallel texts available. The question is then, how can we turn these parallel texts into a useable, organized, and reliable data set? There are two general methods that can be used to collect and organize this data, the old fashioned way by hand or automating the process through a computer. While this paper will touch on both methods, it takes a much deeper dive in the automated processing side and looks at some of the reasons this route was chosen.
InfoGuides: Teaching & Learning Team Workshops & Events: Workshops

This guide provides current information about the University Libraries' Teaching & Learning Team workshops and events.
Improving the Reading Ability of Elementary School Students in Recognizing the Nutritional Content of Carrots Through the Use of Mind Map

Reading ability is a skill to gain knowledge and insight that will increase intelligence. At the elementary school level, it is still quite difficult to foster interest in reading, so that learning media is needed that can support teaching and learning activities. Thus, the purpose of this study was to determine the level of reading ability and understanding of elementary school students about the introduction of nutrients in carrots and the effect of using mind mapping on the learning process. The research method used is quantitative with data collection techniques through pre-test and post-test activities. The sample in this study amounted to 10 fifth-grade elementary school students. Based on the results of the pre-test and post-test activities on the introduction of the nutritional content of carrots using mind mapping, it showed a significant increase in students' reading and understanding abilities with an average increase of 74%. This shows that mind mapping can make the learning process fun and make students pay more attention. Based on the results of this study, it can be concluded that the use of mind mapping can help elementary students in improving their reading skills and understanding the relevant nutritional content of carrots.
Contrasting attentional processing in visual search, object recognition, and complex tasks

Visual attention is known to be involved in task control, yet not much is known about the role of attentional processing in different tasks. Attention is often investigated in visual search, where the task is to localize an object in a scene, while another basic task is to recognize an object, where the attentional processing is largely unexplored. However, we propose visual attention being involved in multiple tasks differently and investigate how visual attention might operate in several complex tasks. The motivation comes from the complexity of real-world tasks. Typical is a continuous interplay between different basic tasks, like first searching an object, identifying it, using it, etc. Based on a recent neuro-computational model of visual attention (Beuth, Doctoral thesis, 2019; Beuth & Hamker, 2015, Vision Research), replicating a large set of neurophysiological data, we selectively disabled different mechanisms that altogether define attention in the general case, and measured performance drops. Analysis was performed on an image data base (COIL-100) and a real-world application (wafer inspection). For visual search, we found that the top-down amplification of specific objects and features is important, as well as the spatial suppression between different places. While for recognition, we diametrically found a significance for the spatial amplification, and for the feature-based suppression. Hence, we observed opposing influences and found out that visual attention operates diametrically in the localization and recognition task in the visual system. In more complex tasks (Object Substitution Masking), composed of visual search and recognition, we found an interplay takes place between both mechanisms, according to the phases of the involved processes. These results illustrate how visual attention may operate in two major task classes, and the model predicts how complex tasks can be solved based on these 'building' blocks to realize the complex task nature of our real-world.
A generalized framework for accelerating exhaustive search utilizing deterministic related-key differential characteristics

The deterministic related-key characteristic in DES can be used to accelerate the exhaustive search in the single-key setting even if an adversary cannot obtain the ciphertexts for arbitrary plaintexts in the related-key model. Inspired by this observation, it has become a common belief that if there exist $$2^m$$ 2 m deterministic differential characteristics for a block cipher with the key size of k, they can be employed to decrease the security to $$k-m$$ k - m bits. The adversary should be able to efficiently partition the key space according to eliminated related keys in order to accelerate the exhaustive search. However, the conventional technique utilized to exploit one deterministic related-key differential characteristic is not extendable. Several deterministic related-key differential properties, regardless of the differences’ values cannot be exploited by applying this technique. In this paper, we describe a precise framework for utilizing several deterministic related-key differential distinguishers, which provides a general methodology to reduce the security of cryptographic primitives. It takes the advantage of deterministic related-key properties. We demonstrate our proposed framework can be used to evaluate the security of block ciphers by presenting straightforward applications of our framework on different variants of block ciphers. In particular, we present a new attack on the well-known FX and Even–Mansour constructions. The latter is quite simpler than the former.
Improved Chroma Resampling Algorithm Based on Interpolation Method

An improved algorithm based on bilinear interpolation was proposed. It could deal with the shortcoming of chroma component information loss in the original video image chroma resampling algorithm. With the bilinear interpolation method, the chroma component matrix in the YCbCr color space was scaled in both the horizontal and vertical directions. It could reduce information loss while meeting the corresponding standard data format. The new algorithm could retain more information from the source image’s color, which improved image recovery. In addition, the paper proposed a new format with a higher compression ratio on the basis of ensuring visual performance. Experimental results showed that, compared with the original chroma resampling algorithm, the processing result of the improved algorithm had a significant improvement in the performance of parameters such as composite peak signal noise ratio (CPSNR).
Hygienic Assessment of Educational Programs During the Pandemic

In 2020, the World Health Organization announced the beginning of a pandemic of the new coronavirus infection COVID-19. To prevent the spread of viral infection in all regions of the Russian Federation, a self-isolation regime was announced. In this connection, on March 15, 2020, the Ministry of Education recommended that all higher educational institutions carry out educational activities using modern information and telecommunication technologies that allow learning at a distance without direct contact between the teacher and the student - distance learning. The introduction of self-isolation and the transition to distance education led to the need to change the usual way of life, and adapt to the prevailing conditions of educational activity. The aim of this work is to analyze data on the impact of distance learning on the quality of students’ health. Methodology and research methods. The work was performed using a descriptive method by collecting, summarizing and analyzing literature data regarding the problem being studied. Results. The authors have highlighted the main characteristics of distance education. An analysis of modern sources has shown that, given the modern achievements in the field of telecommunication technologies, distance education is not fully capable of replacing the classical one; there are negative aspects both in the quality of training of students and in relation to the health of all participants in the educational process. Conclusion. With the continuation of teaching using modern information and telecommunication technologies, it is necessary to develop and implement programs aimed at preserving health and adapting to the conditions of distance education.
The Library Liaison’s Guide to Collection Management

"The Library Liaison’s Guide to Collection Management." Technical Services Quarterly, 38(3), pp. 336–337
IMPROVING THE SEVENTH GRADE STUDENT’S NARRATIVE WRITING ABILITY THROUGH STORY TELLING VIDEO AT SMP N 1 KEDIRI

The study aims at figuring out the improvement of writing skill of the eighth grade students of SMPN 1 Kediri through Storytelling with video. Storytelling is a technique of teaching writing. The under taking of the research was mainly based on the preliminary study. The subjects were the seventh grade students of SMPN 1 Kediri in academic year 2020/2021. There were 30 subjects which consist of 21 males and 9 females. The design was classroom action research design. The research was carried out through implementing storytelling with video in two cycle and the obtained data was analyzed qualitatively from the pre-test, post-test 1 and post-test 2; then qualitatively from questionnaire. The result showed that there was significant improvement of the subjects skill in writing descriptive paragraph, pre-cycle (72.7) up to cycle I then, it improved in cycle II (87.8). In addition, the questionnaire proved that the subjects responses toward the implementation of storytelling with video was positive during the teaching learning process. In short, the study showed that writing skill of the eighth grade students of SMPN 1 Kediri in academic year 2020/2021 can be improved through storytelling with video.