Reviewerly/exHarmony huggingface dataset:
Quantifying data imbalance using Exponential f-Divergence

In this study, a new measure of imbalance is introduced in order to compute the extent of imbalance for multiclass data. In the case of binary datasets, the Imbalance Ratio (IR) can be used to measure the amount of imbalance. However in the case of multi-class datasets, since it only takes into account the frequency of the most frequent majority class and the least frequent minority class, it fails to encapsulate any properties from the intermediate classes. An imbalance Degree (ID) was proposed to overcome the issues of IR by considering information from the intermediate classes as well. Nevertheless, it required us to choose a distance metric that largely influenced the results and could lead to unfavorable results. It is also assumed that the number of minority classes impacted the extent of the imbalance without considering their individual contributions, which is not correct. Thus, ID cannot be chosen as an authentic metric if this assumption is breached. Furthermore, another metric called Likelihood Ratio Imbalance Degree (LRID) was proposed to make the metric independent of the number of minority classes in the data. However, it considered the imbalance to be directional and assumed both positive and negative values for individual contributions from classes. In this study, we obtain a more authentic procedure to measure the extent of imbalance extent using statistical divergence from balanced class distributions.
Multi-blockchain Scheme Based on IoT and Smart Contracts in the Agricultural Field for Data Management

The Blockchain represents data that is both structured and shared. Instantly, it is a current approach of handling distributed databases by a group of people. The purpose is to collect data, construct a digital ledger, and distribute it to each participant using P2P techniques. Blockchain technology gets employed in various industries, including government, transport, commerce, and healthcare. This paper will focus on using Blockchain in the agricultural supply chain, particularly the multi-Blockchain strategy for increasing performance and yield tracking. This type of architecture necessitates the presence of at least two chains in one system. This study will present a new theoretical architecture based on past investigations; the article examines how to adjust the approach and lists the many phases involved. The new framework will eliminate the mistakes that have plagued past studies. First, sensors get used to supply us with the recording data sets. We then store our data in blocks using the multi-Blockchain structure. Then, to control all transactions and make choices based on the criteria in the source code of these automated contracts, we create Smart Contracts. This technique should be more effective than cloud apps or simply storing data on a Blockchain.
Securing Caesar Cryptography Using the 2D Geometry

Cryptography has now become a science in its own right, where, mathematics, computer science, and sometimes even physics, are meeting to allow all secured exchanges between distributed systems, by ensuring confidentiality, authenticity and integrity of digital information. Throughout this paper, a method of securing the most famous crypto-system of antiquity will be presented, using the 2D geometry. Being the origin of cryptography, the Caesar cryptosystem, presented a very basic method to encrypt messages. However, with the arrival of cryptanalysis, this method is no longer up to date. In fact, a simple exhaustive search can break the entire cryptosystem, and thanks to the algorithm of hybrid cryptography, inspired by the 2D geometry, we overcome this limitation, offering a method to expand the space of possible keys and manipulate the encrypted message so that any attacker cannot detect the original message.
Cyber Suraksha: a card game for smartphone security awareness

Purpose The frequency and sophistication of cybercrimes are increasing. These cybercrimes are impacting government and private organizations as well as individuals. One of the countermeasures is to improve the cyber hygiene of the end-users. Serious games or game-based learning has emerged as a promising approach for implementing security education, training and awareness program. In this paper, the researchers propose a tabletop card game called Cyber Suraksha to increase threat awareness and motivate users to adopt recommended security controls for smartphone users. Cyber Suraksha provides an active learning environment for the players. This paper aims to provide the details of the design and evaluation of the game using a between-subjects design. Design/methodology/approach The researchers have used constructive learning theory and the Fogg behaviour model (FBM) to design a tabletop card game called Cyber Suraksha. The researchers evaluated the game using a between-subjects design. The participants' responses in the control and intervention groups were collected using the risk behaviour diagnosis scale. Pearson’s Chi-Square test with a 5% significance level was used to test the hypotheses. Findings The results indicate that the game is enjoyable and fun. Cyber Suraksha game effectively motivates users to adopt the recommended security control for the targeted behaviour. The results indicate that the participants in the intervention group are 2.65 times more likely to adopt recommended behaviour. The findings of this study provide evidence for the effectiveness of hope and fear appeals in improving cybersecurity awareness. Research limitations/implications The generalizability of the study is limited because the sample size is small compared to the total number of smartphone users in India, and only students from computer/IT UG programs in India are used as participants in this study. Practical implications This study uses hope and a fear appeal to design an effective serious game. It also demonstrates using the FBM and constructive learning principles for effective serious game design. Cyber Suraksha is effective for the student group and may be tested with other age groups. Originality/value To the researchers' knowledge, there are no serious games for cybersecurity awareness focusing on the threats faced by smartphone users based on FBM and constructive learning theory. This research used hope along with a fear appeal to motivate smartphone users to adopt recommended security controls.
A design anthropology of collaborative making: Exploring shoemaking and embroidery practices

Design anthropology covers various design practices and research. While some researchers/pactitioners rely on anthropological insights to enhance design, others use design as a tool to gain anthropological knowledge. This paper explores the symbiotic relation between design and anthropology and proposes a ‘design anthropology of collaborative making’. It contributes to gaining insights in plural ways of knowing while providing contextual insights that inform design practices. Starting from a common skill from an ‘in-habitat’ position, we consider both perspectives and invite collaborative engagement between people and materiality. Based on collaborations with the San community in Namibia on shoemaking and with Syrian refugee women through embroidery, we explore the added value of this tactile engagement through making to bridge theory and practice.
Semantic Segmentation Architecture for Text Detection with an Attention Module

Scene text detection is a very challenging problem due to its variability in size, font, color and orientation. The trend of research is to create models which detect bounding box coordinates around text areas inside an image. However, in this work we try to address this problem in a semantic segmentation way by creating masks to highlight these zones. Our model is a fully convolutional network, inspired by the UNet architecture. It is a pixel-wise classification framework which outputs a prediction map having the same width and height of the input image. Pixels inside this map are labeled either belonging or not to a text area. To increase our model performance, we make use of transposed convolution to upsample the feature maps in the decoder part. We add an attention module to our architecture in order to recalibrate channels-wise features which help us to increase the overall performance by 1%. We also propose to use the intersection over union loss to solve the imbalance problem since text generally occupies a very small amount of pixels inside images. We conduct the train and test experiments on the ICDAR 2015 dataset. Our model reaches a MeanIoU score of 44.20%.
Optimal Route Planning and Power Management for Hybrid UAV Using A* Algorithm

For the practical application of a gas-electric hybrid UAV, algorithms are needed to synthesize joint motion and power plans that satisfy path and energy constraints. Specifically, this paper considers the problem of finding an energy-optimal flight path toward a destination under the flight path constraints and battery-only operational constraints. Standard techniques such as reinforcement learning, or Dynamic Programming are computationally expensive. This paper investigates the performance of the A* algorithm on this problem to reduce the computational complexity and, by extension, the computational time. By constructing the objective as the minimization of unrecoverable energy expenditures, the A* algorithm, Dijkstra’s Algorithm, and a proposed iterative A* solution, were successfully implemented for this problem. The developed optimal control policies were observed to satisfy regions with battery-only restrictions and match the total route cost through comparison to the Dynamic Programming solution of the same environment. Results show that the use of the A* algorithm and Dijkstra’s algorithm obtain an optimal solution of 1.64-8.85 times faster than the optimal solution obtained through Dynamic Programming.
Blockchain and IoT for Real-Time Decision Support for the Optimization of Maritime Freight Transport Networks

In the world of import-export, the transport of goods by sea is a must. Indeed, nearly 90% of world trade is carried out in this way. For this reason, companies and states compete in ingenuity to invent the container transport of tomorrow. The objective of this work is to study the role of the IoT and blockchain smart contracting technologies in the maritime transport networks of goods and whether it would bring a real added value in the case of a Logistics company. The results showcase the potentials of using blockchain smart contracting in the environment of maritime freight transport networks.
Tracking Methods: Comprehensive Vision and Multiple Approaches

Tracking of human beings, as well as objects, becomes a great challenge currently presented, it aims to understand the fundamental principles of objects and human beings detected to associate them with robust systems for treaties and draw the desired conclusions. Tracking generally uses several methods such as graph theory, technological tools, Internet of Things (IoT), Big Data, and artificial intelligence, where it maintains several hypotheses to help tracking objects or human beings. In this article, we propose the tracking methods proposed in this direction, and finally, we analyze and discuss the various results obtained.
A Comparative Study of Vulnerabilities Scanners for Web Applications: Nexpose vs Acunetix

As known, everyone uses web applications to make purchases, transfer funds, upload data, etc. Meanwhile, the security of these web applications has become a significant challenge due to various vulnerabilities in web applications such as XSS, SQL injection (Second order), and many others. For this aim, we have penetration tests, that plays a very important role to detect those vulnerabilities, and it is called a simulated network attack, in which professional ethical hackers break into the company’s network to find vulnerabilities using tools that have been proven to uncover different types of vulnerabilities in a very short time. In recent years, cybersecurity has experienced a different kind of vulnerability detection based on AI, more specifically machine learning. This challenge has led cyber defense engineers to create different modules to handle this. In this article, we will do a comparison between two penetration testing tools while scanning the same target and discovering 3 ranking vulnerabilities and see some approaches.
Single-camera Three-dimensional Deformation Measurement using Data-driven Reduced Order Model

The present study proposes a single-camera 3D deformation measurement method using the data-driven reduced-order model and evaluates its accuracy using a foundation-shape model. Photogrammetry is applied to the stereo camera measurement and the training data for a reduced-order model was constructed. The singular value decomposition is applied to the training data and the linear reduced-order model was constructed. Three types of the linear regression model were proposed and their estimation accuracy was evaluated. Regardless of the model, single-camera deformation measurement can measure the three-dimensional coordinates with an accuracy of less than 0.4~mm. Therefore, the proposed method has the possibility to perform the simultaneous measurement of the PSP and deformations.
Machine Learning Models for Online Anomaly Detection in Flight Operations

Anomaly detection in flight operations data is a prominent approach to delivering actionable information for improved aviation performance as anomalies are often related to critical safety events or inefficient operations. In particular, the online detection of anomalies from streaming data provides the basis for novel real-time monitoring and alerting tools that can lead to more proactive and efficient decision-making during tactical operations. In this paper, we develop machine learning models for the online detection of flight trajectory anomalies from aircraft surveillance data. We focus on a specific type of operationally significant anomaly: go-around maneuvers. Air traffic controllers typically have to react quickly upon the occurrence of a go-around in order to reintegrate the aircraft into the landing sequence and maintain a safe and expeditious traffic flow. We develop a data-driven approach to anticipate such anomalous events for real-time air traffic control decision support. For this, two models are learned and compared in terms of predictive and computational performance: a Gaussian Mixture Model and a Temporal Convolutional Neural Network Model. The models are found to behave similarly, being able to anticipate 67% of the go-arounds with a false positive rate of less than 8%. Moreover, we find that the inclusion of weather features in both models allows for earlier prediction of go-arounds during the approach. In particular, the Gaussian Mixture Model is able to predict 78% of the go-around maneuvers correctly as early as 50 seconds from the runway.
A Decision-Making Model Based on a High-Level Ontology in Context of a Smart Home

The Smart Home is a residence equipped with computer technology that assists its inhabitants in the various situations of domestic life by trying to optimally manage their comfort and safety by action on the house. The detection of abnormal situations is one of the essential points of a home monitoring system. These situations can be detected by analyzing the primitives generated by the audio processing floors and by the apartment's sensors. We propose in this paper a Inference and decision-making model based on a high-level ontology.
Approaching Complex Societal Problems Tied to Aviation

From climate change to health inequity, humankind faces complex societal challenges that aviation can both exacerbate and help to alleviate. In this paper we describe our approach to identifying and approaching complex societal challenges with existing or potential ties to aviation as part of the NASA Convergent Aeronautics Solutions (CAS) project. We detail three example challenges that we have done preliminary work on: enabling access to healthcare, supporting resilient rural communities, and ensuring supply chain wellness. Our approach to understanding and addressing such challenges involves iterative human-centered, systems-oriented, and futures-based design. We provide a discussion of cross-cutting characteristics of complex societal challenges that appear fruitful for aviation to address and detail the lessons we have learned thus far in approaching such challenges.
Влияние социальнодемографических факторов на самоотношение студентов

The purpose of the study presented in this article is to investigate the influence of gender, level of economic well-being, university status, origin and year of study on the self-attitude of modern students. The study sample consisted of 315 full-time and part-time students, aged between 18 and 30 years (M = 20.5), from higher education institutions in the Republic of Moldova. The results showed that the self-attitude of private university students is slightly higher than that of state university students, does not represent a significant difference. Female students’ self-attitude is higher in comparison with male students. Middleincome holders have the highest values of self-attitude components. The origin also affects the students’ self-attitude; students from urban areas have a better attitude toward themselves than those from rural areas. Regarding the course of study, the highest indicators of self-esteem parameters are among first-year students, the difference between second-year students and third-year students is very small.
Formation Flight of Multiple UAVs Using Artificial Potential Field Algorithm

The present work deals with the design and validation of a formation flight control algorithm able to manage five aircraft simulated using a linear model representative of a a medium altitude, long endurance unmanned airplane. The proposed approach is based on the artificial potential field and it is validated both in bi-dimensional and three-dimensional spaces. Even though the aircraft models are time-invariant, the external environment is simulated to be time-variant. In fact, within the simulation it is possible to simulate intruders, or object moving with its own dynamics, and no-fly zones.
Attention guided domain alignment for conditional face image generation

Despite the great success of Generative Adversarial Network (GAN) in face image generation, it is still a challenge to generate a faithful yet high-fidelity face image given an exemplar image and a conditional input from a distinct domain (e.g., a semantic segmentation mask or an edge map). Existing methods learn image-level features to align distinct domains in an intermediate domain, which ignore the spatial relationship of the facial semantic parts and therefore lead to semantic mismatching problems. In addition, it is computationally expensive to establish dense correspondences in the intermediate domain, especially for high-resolution face images. To address these problems, we propose a novel attention guided domain alignment method for conditional face image generation, which aligns two domains directly under the guidance of the local attention learned from semantically similar face parts. In particular, we assign a dedicated index for each feature block and adopt a top-k ranking operation to correspond block-wise features in two distinct domains, which exploits the spatial relationship of facial parts and preserves the texture structure during alignment. The local attention is then learned from the retrieved blocks, which reduces computation complexity substantially and therefore enables to build alignment in a high resolution. The aligned features are finally fused with adaptive weights learned from their long-range correlation coefficients, which capture the semantic coherence of the style features between the two domains. Extensive experimental results on the CelebAMask-HQ dataset demonstrate that the proposed method is superior to the state-of-the-art methods.
S-KMN: Integrating semantic features learning and knowledge mapping network for automatic quiz question annotation

Quiz question annotation aims to assign the most relevant knowledge point to a question, which is a key technology to support intelligent education applications. However, the existing methods only extract the explicit semantic information that reveals the literal meaning of a question, and ignore the implicit knowledge information that highlights the knowledge intention. To this end, an innovative dual-channel model, the Semantic-Knowledge Mapping Network (S-KMN) is proposed to enrich the question representation from two perspectives, semantic and knowledge, simultaneously. It integrates semantic features learning and knowledge mapping network (KMN) to extract explicit semantic features and implicit knowledge features of questions,respectively. Designing KMN to extract implicit knowledge features is the focus of this study. First, the context-aware and sequence information of knowledge attribute words in the question text is integrated into the knowledge attribute graph to form the knowledge representation of each question. Second, learning a projection matrix, which maps the knowledge representation to the latent knowledge space based on the scene base vectors, and the weighted summations of these base vectors serve as knowledge features. To enrich the question representation, an attention mechanism is introduced to fuse explicit semantic features and implicit knowledge features, which realizes further cognitive processing on the basis of understanding semantics. The experimental results on 19,410 real-world physics quiz questions in 30 knowledge points demonstrate that the S-KMN outperforms the state-of-the-art text classification-based question annotation method. Comprehensive analysis and ablation studies validate the superiority of our model in selecting knowledge-specific features.
Agile Practices in Iteration Planning Process of Global Software Development

Global software development (GSD) term refers to a method of developing software in which stakeholders from many locations and backgrounds participate in the software development life cycle. Software companies use GSD to profit from its many advantages, including cheaper development costs, faster cycle times, accessibility to remote experts, and proximity to the local market. Iterative software development is the goal of the agile software development approach. Customers can add or remove requirements and adjust their priorities after each iteration. This allows them to be a part of the team throughout the project and affords them a lot of flexibility. Therefore to preserve these benefits in the GSD context, the Agile Planning process should consider the many imposed distances, such as geographic, temporal, cultural, language, and organizational ones. This paper focuses on the Iteration Planning level in Agile Global Software Development projects by offering the best agile practices recommended for usage in GSD Iteration Planning based on existing literature.