Reviewerly/exHarmony huggingface dataset:
Attribute-Based Data Sharing in Smart Healthcare Environment

In the modern age, Internet of Things (IoT) has gained attention towards developing a smart healthcare system. IoT mainly consists of physical devices embedded with various sensors and actuators. Such smart devices play a vital role in the health sector. In the healthcare environment, the engagement of medical personnel and doctors can be observed very frequently due to increasing the number of patients day by day. So, there is a requirement of role delegation for the treatment of patients in an efficient way. Additionally, medical data security and privacy must be maintained during the role delegation process. Attribute-based encryption (ABE) plays an important role in securing medical data in a smart healthcare environment. Therefore, we propose a secure data sharing technique using ciphertext policy ABE to share Electronic Health Records (EHR) securely in a smart healthcare environment. We use bilinear pairing to perform various operations. The security of the proposed scheme depends upon the Decisional Bilinear Diffie-Hellman (DBDH) assumption. The implementation of this scheme shows that the proposed scheme provides better results.
Vowel and non-vowel frame segmentation based digital speech watermarking technique using LPA method

With the increased use of the Internet in everyday life, data transmission over the internet needs to be secure, so it necessitates the adoption of watermarking. Watermarking is the best solution for securing digital content and ensuring the integrity of the original data. It also certifies digital content and provides the best form of concealing information for permission. In many circumstances, the information is problematic as the use of digital content evolves rapidly. In this paper, we have proposed a novel speech watermarking method based on vowel and non-vowel frame segmentation using the Linear predictive analysis (LPA) method. The different vowel frame segments have been used in the LPA method to estimate the predicted values. The proposed model has used the LPA method, which allows for a more adaptive and skilful demonstration of a novel digital speech watermarking method. Afterwards, the specific locations have been chosen to embed the minimum value obtained from the grayscale watermark image. For the evaluation purpose, we have considered objective and subjective measures. The average SNR, MOS, and BER values of the proposed method are 45.68 dB, 4.55, and 0, respectively. The imperceptibility of watermarked speech and extracted watermark signals using the proposed method have approximately 48 dB, and 59 dB, respectively. The results show that the proposed speech watermarking procedure is more secure, powerful, and subtle than existing watermarking techniques because it reduces time and memory significantly.
State-of-the-art Analysis on the Design Parameters of Distributed Transformer using IoT

The Internet of Things (IoT), which allows autonomous items to share data over the Internet, has created a lot of interest in Machine-to-Machine communication. The design and implementation of real-time transformer monitoring and fault detection, as well as the recording of critical dispersion in the transformer operating indicators such as current, voltage, oil level, transformer temperature, and frequency, are described in this study. With this project, you may reduce working effort while improving accuracy, stability, and efficiency. Sensors are used in this project to monitor critical equipment aspects including fire, gas, and temperature. This data is sent to a microcontroller, which checks for parameter limits before transmitting it to the Adafruit IoT web server over Wi-Fi. The substation operator may be prompted to take suitable steps if a warning or alert indicates that the transformer is overloaded and at risk of excessive insulation ageing or loss of life.
EVSec: An Approach to Extract and Visualize Security Scenarios from System Logs

Logs, a.k.a. execution traces, provide a glimpse into the functionalities of running systems that have poor, incomplete, or outdated documentation. Logs contain a rich amount of information that can be used to facilitate troubleshooting/debugging, track events, detect security breaches, maintain regulatory requirements, and profile user behavior and workload. Driven by the growing complexity of today’s software platforms, reverse engineering of high-level models from system logs has gained momentum in recent years. In this paper, we introduce EVSec, an approach to extract and visualize security scenarios from system logs. The collected logs are first merged, filtered, labeled, and segmented into execution phases. The resulting phases are then visualized using the ITU-T standard, Use Case Maps (UCM) notation, extended with security annotations. We show the applicability of our proposed EVSec approach using two real-world security features, namely, Cisco IOS Login block and Cisco Unicast Reverse Path Forwarding (uRPF).
Poster: Blockchain-Envisioned Secure Generic Communication Framework using Signcryption

We aim at building a generic future-generation, secure communication framework on top of blockchain using signcryption. A publicly verifiable signcryption scheme not only ensures data confidentiality and authentication, but also enables messages to be verified or audited non-interactively. With blockchain as trustworthy computation and storage services, the proposed secure communication framework achieves tamper-resistance, non-repudiation, availability and public verification. Moreover, we consider both large-size data (e.g., file) and small-size data (e.g., cryptographic hash or key) transfer paradigms, where blockchain and signcryption schemes are seamlessly combined. In particular, IPFS is used as a hash oracle and decentralized storage platform in big data transfer.
Improvement of Object Detection Performance for Enemy Tanks Using the Combination of Data Augmentation Models

With the development of artificial intelligence technology, it is continuously being applied in the defense field. In particular, deep learning-based object detection technology is treated as a groundbreaking technology in the field of defense surveillance and reconnaissance. However, there are limitations to use deep learning-based object detection technology because it is hard to get enough image data of enemy weapon systems. To overcome this challenge, this paper studies the improvement of object detection performance for enemy tanks using the combination of data augmentation models. Experiment results show that the combination of selected data augmentation models improves object detection performance(especially SinGAN model is effective). The result indicates that the data augmentation in the field of defense surveillance and reconnaissance needs to be studied since the result of combining all data augmentation models would not necessarily be good.
New Media College Students’ Education Evaluation System Based on Improved CW-CPCC Algorithm

The strengthening of comprehensive quality education of college students in colleges and universities has been more and more concerned and accepted by people. The educational evaluation of college students should not only conform to the objective reality but also ensure that it is effective. However, the evaluation indicators in the current education evaluation are grayscale, qualitative, and difficult to assess. Aiming at this problem, this study proposes an improved CW-PPCC-based evaluation algorithm for college students’ education, which quantifies the qualitative indicators that are difficult to measure in the form of data, using AHP and data envelopment. On the basis of big data, the correlation coefficient of product distance quantitatively evaluates the quality of undergraduate education and realizes an effective evaluation of the education quality of college students. The experimental results show that the algorithm can effectively realize the evaluation of education quality, and has high accuracy and high application value.
Indigenous Communication's Role in Traditional Birth Attendants in Maternal Health Practices

Indigenous communication is an indispensable medium of communication in the socio-cultural tradition of Africans. These systems have been used to promote co-operation, mobilization, and participation among African people. This chapter takes into account indigenous communication roles of traditional birth attendants in maternal wellbeing and maternal health practices during pregnancy and childbirth in both Nigerian and South African communities. The significant motivation behind the examination is to assess the indigenous communication role of traditional birth attendants in maternal health practices in the two nations through empirical studies. After careful review of the studies, it is shown and discovered that TBAs in both Nigeria and South Africa assist pregnant women based on their religious or traditional beliefs. The researchers therefore recommend that health professionals should train TBAs in modern maternal health practices. This will go a long way to help them know their restrictions and possibly refer mothers and their children for emergency services and attention as needed.
Data Sharing Approach for Personal Health Record by Session Password and Access Key in Cloud Storage

For the safe sharing of personal health record (PHR) in the cloud, a safe sharing method is built using the proposed session password and access key. The method is secure for doctors to share the PHRs and also gain rights to the patient’s medical history while maintaining their privacy. The proposed data sharing technique strengthens and secures the system, and it has been tested in a variety of circumstances. Furthermore, compared to the traditional data sharing approaches, the proposed method needs less cost, time, and memory. The proposed method utilizes less communication cost, time, and memory of 2123 bits, 21 ms, and 1234 bytes, respectively. It satisfies a variety of security properties, including tamper resistance, transparency, and decentralization.
The delivery of large-scale software products through the adoption of the SAFe framework

The paper analyses the Scaled Agile Framework (SAFe) which is currently one of the most important agile scaling frameworks, being adopted by the software development companies to maintain a competitive advantage in the digital age and, at the same time, it represents a way to involve all the organization departments in delivering technology-based solutions. Through this framework, functions like marketing, sales, legal, finance adopt Agile and Lean practices to achieve greater transparency and align project objectives with business goals. As every business is becoming a software business, building solid and automated models is essential to enable and sustain a large-scale software delivery. The SAFe adoption steps are analyzed to identify the prerequisites needed for the organizational transformation. This paper provides a case study presenting the implementation of SAFe at a global software development company. This study provides relevant information for both practitioners and academics. The examined framework is based on robust theories and offers tools that software companies can use to increase the efficiency of their software delivery model. Even if the assessed framework is partially adopted within an organization, its core principles, tools, and techniques enhance the software delivery practices.
DEVELOPMENT OF VALUE-SEMANTIC COMPETENCE OF UNIVERSITY STUDENTS IN THE PROCESS OF TEACHING A FOREIGN LANGUAGE

In modern conditions, the importance of language education is due to the diversity of languages and cultures, which are one of the most valuable elements of the world, European and all-Russian cultural heritage.The use of the competence-based approach, which is being introduced into the practice of university education everywhere, is connected with the fact that competence is becoming a new unit of measurement of a person's education, his creative potential, independence and competitiveness.The core of the competency-based approach is value-semantic competence, since the formation of a system of values for future specialists is an obligatory part of the educational process at university.Value-semantic competence refers to the sphere of worldview, it is associated with the student's value ideas, his ability to see and understand the world around him, navigate in it, realize his role and purpose, be able to choose target and semantic settings for his actions and deeds, make decisions.The individual educational trajectory of the student and the program of his life as a whole depend on this competence.The purpose of the study is to disclose the mechanism of reflection of conventional values and anti-values through language assessment tools, which is important when teaching a foreign language at university in the context of intercultural communication.Being the most important components of human culture, values, along with norms and ideals, are reflected in the language.Values, national-cultural traditions and peculiarities of perception of the world are most capaciously and distinctively manifested in phraseological units that are one of the most interesting and productive objects of comparative research in the axiological aspect.In the course of the study, it is revealed that the implementation of the assessment occurs when the object is correlated with the value, reflecting the attitude of native speakers to the object in terms of compliance or non-compliance of its qualities with certain value criteria.The estimated value of a phraseological unit (positive or negative) is determined with the help of cultural information related to the etymology of the phraseological unit.As a result of the research, it was found that studying the picture of the world reflected in the mirror of the axiological phraseology of two languages allows us to compare national stereotypes and life values of representatives of Russian and German societies.This knowledge makes it possible to successfully carry out intercultural communication.Russian and German axiological phraseological units with a positive or negative assessment reveal the content of conventional values and anti-values.This fact should be drawn to the attention of students studying a foreign language.The formation of the system of values of future specialists becomes an indispensable component of the educational process at university, which includes presentation, comprehension, acceptance, realization, consolidation, ideologization of values and value orientations.
Why the digital classroom is a bad model for digital learning

The pandemic has compelled educational institutes across the world to hastily adopt, adapt and cope with digital learning by developing ‘digital classrooms’, virtual learning environments replete with sophisticated technological features. The eagerness of the intent to ramp up technological infrastructures, however, is not reflected in the rethinking or redesigning of pedagogies for digital learning. Technocratic ambitions often have very little, if anything, to do with pedagogy. Moreover, the digital classroom is ‘global’ by design, and the ethnocentric framework wrought upon by such designs impedes the decolonizing of the curriculum. It depletes local pedagogic cultures: local histories and cultures, political and moral economies. This article argues that imagining the digital classroom a-temporally and a-culturally, typically from a technocratic point of view, signals a tryst with (techo-)orientalism in its new avatar.
Composition and «pointless» painting in system of nonconventional approaches to creative process

In article the points of view on «pointless» painting as on one of aspects of abstract art and nonconventional approaches to creative process are analysed.
Game Audio Impacts on Players’ Visual Attention, Model Performance for Cloud Gaming

Cloud gaming (CG) is a new approach to deliver a high-quality gaming experience to gamers anywhere, anytime, and on any device. To achieve this goal, CG requires a high bandwidth, which is still a major challenge. Many existing research pieces have focused on modeling or predicting the players’ Visual Attention Map (VAM) and allocating bitrate accordingly. Although studies indicate that both modalities of audio and video influence human perception, a few studies considered audio impacts in the cloud-based attention models. This paper demonstrates that the audio features in video games change the players’ VAMs in various game scenarios. Our findings indicated that incorporating game audio improves the accuracy of the predicted attention maps by 13% on average compared to the previous VAMs generated based on visual saliency by Game Attention Model for CG. The audio impact is more evident in video games with fewer visual components or indicators on the screen.
Analysis of Flood Peaks Using The Mean Annual Flood Method

The Kedang Pahu river is one of the tributaries of the Mahakam river. The research plan is located in Damai District, West Kutai Regency, East Kalimantan province. Recently, the Damai District and Damai Seberang areas have flooded activities that have caused the surrounding settlements to flood into residential areas and block existing road access. Planning analysis and knowing the annual flood elevation is essential. The analysis uses the method of calculating the mean annual flood (MAF) to search for the average annual flood discharge data and the search for the average annual elevation. Data validation using a simple linear regression method produces a correlation coefficient of 58.67%, or R-value = 0.5867. The analysis results in the value of Q1 or the 1st year period, the mean annual flood rate of the average annual flood discharge is 2576.0695 m³/second and the value associated with the magnifying factor (GF) is the average annual flood discharge rate of Q5=3014,00 m³/ sec, Q10 = 3529.22 m³/sec, Q20 = 4095.95 m/sec, Q50 = 5049.10 m³/sec, Q100 = 5847.68 m³/sec, Q200 = 6852.34 m³/sec, Q500 = 8423.75 m³/sec &amp; Q1000 = 9917.87 m³/sec. The analysis results at HEC-RAS 5.07 based on manning analysis showed the elevation values were Q1=18.47m, Q5=18.85m, Q10=18.86m, Q20=19.18m, Q50=19.74m &amp; Q100=19.99m. Researchers only show elevations up to Q100 or the 100th year because of limited data and the reviewed data accuracy.
Optimal foraging algorithm with direction prediction and Gaussian oscillation for constrained optimization problems

Optimal foraging algorithm (OFA) is a newly stochastic optimization technique and is famous for its computational accuracy. However, the high computational accuracy leads to slow convergence speed. Experimental results demonstrate that OFA is good at unimodal functions but poor at multimodal functions. To improve these drawbacks, in this paper a novel modified OFA with direction prediction and Gaussian oscillation, named OFA/P&G is introduced. In OFA/P&G, a transition matrix is constructed when a new global optimum is found to generate the candidate individuals. If the current global optimum does not change, the Gaussian oscillation is employed in a low probability and OFA update method is used in a high probability to generate the candidate individuals. The superior performance of OFA/P&G is verified on the 12 CEC2017 benchmark functions, 13 constrained benchmark functions and 5 engineering problems. Experimental results demonstrate that OFA/P&G outperforms other comparative algorithms. Finally, a real-world problem, drilling path optimization, is solved by OFA/P&G.
Research on Complex Multiconstraint Path Planning Based on ACA Hybrid Algorithm

Aiming at the multiconstraint complex problem of path planning, a hybrid algorithm with fast convergence and fast obstacle avoidance is proposed. According to the actual aircraft, a multiconstrained path cost model with seven components is constructed and defined the parameters and penalty factors of each part. To improve the sensitivity of each part of the trace planning cost function, according to the inconsistency of the cost index unit and measurement range, the expert scoring method is used to configure and normalize the index with weight. Make full advantage of the ACO algorithm and the artificial potential field algorithm to form the ACA hybrid algorithm, a pheromone heuristic function improvement method is proposed, and a control factor is introduced. The proposed model and planning algorithm are simulated based on the MATLAB platform environment, and it is shown by simulations that the hybrid algorithm has obvious advantages in terms of motion trajectory smoothness, number of iterations, and minimum path cost .It well solved the practical application problem of fast path planning convergence and fast avoiding obstacles in the complex environment.
Demonstration of Integration of Blockchain in IoT

In the modern era, blockchain technology has achieved a substantial growth with rapid research developments. Blockchain technology is widely used in the field of cryptocur-rency. However, it can also be applied in other field such as internet of things (IoT). In IoT, data security and integrity is a prime concern where many problems exist such as data reliability and unauthorized access. It creates huge problem in deploying an IoT system on a smaller or larger scale. Hence, this paper reports a possible integration of blockchain technology in IoT to create a secure network. To demonstrate this, the game of Tic-Tac-Toe is implemented by following the rules of blockchain. The main goal of this implementation is to explore the application of blockchain in IoT and practically merge these two fields to get the service of IoT with the security and integrity that the blockchain technology promises.
Social Distancing and Face Mask Detection For Covid Prevention

The novel virus additionally carries Covid-19 which has a profound outcome of numerous sections in lots of regions and public sectors are growing around the arena to sell viral media coverage. This is a small step to carrying a face mask, following far from the network will keep many lives because the unfolding of the virus novel may be reduced. This article carries records on public dissemination and face protecting approximately instances of sicknesses consisting of the radical virus that may be resolved through worrying for its social distribution/management. This became used to provide Mask Detecition the use of OrenCV, Keras/TensorFlow, and Deer Learning. This application may be effortlessly integrated and established on a whole lot of high-density embedded gadgets with the use of MobileNetV2 architecture. The gadget will come across faces mask on various instantaneous videos, many photos and images.
An Enhanced Fast – High Utility Item set Mining Method for Large Datasets

High Utility Itemset mining is considered one of the critical and challenging problems in data mining. The existing mining framework is limited to analyzing occurrence counts of items in the Database. However, this framework applies a single minimum utility threshold value that fails to consider different item characteristics. Recent methods of association mining focused on finding the high utility itemsets instead of frequent itemsets generations. Some utility-based mining methods that is Faster High Utility Itemset Mining (FHM), High Utility Itemset Miner (HUI-Miner), Direct Discovery of High Utility Patterns (D2HUP), Utility Pattern Growth (UP Growth &amp; UP Growth+) are studied for the generation of high utility itemsets generations. Existing HUI mining methods are effectively generating HUIs. However, developing a faster and memory-efficient HUI mining method is required. For this purpose, this work develops an Enhanced Fast - High Utility Itemset Mining (EF-HUIM) method for the faster generation of high utility itemsets and respective association rules.