Reviewerly/exHarmony huggingface dataset:
Research on facial landmark detection algorithm based on improved attention mechanism

In recent years, facial landmark detection has assumed an important role in various fields. However, the current facial landmark detection algorithms are still lacking in recognition accuracy. In order to solve the above problem, this paper uses Ghost bottleneck to replace the original bottleneck on the basis of the original model of PFLD model, and adds and improves the CBAM attention mechanism. The improved PFLD model increases the ability of the model to extract facial landmark and improves the accuracy of the algorithm. The improved model has high accuracy and low parametric number and improves the accuracy of facial landmark detection. It also provides a new idea for facial landmark detection task.
Authentication Scheme Using Honey Sentences

Password-based authentication has dominated authentication schemes for decades because of its usability. However, password-based authentication is vulnerable to password-guessing attacks. To mitigate this attack, users have to choose a good password that is hard to guess. However, a password secured enough from password-guessing attacks will be difficult for users to memorize. One of the prior works that increase the complexity of password-guessing attacks without decreasing the usability is honey encryption (HE). HE produced a fake plausible-looking plaintext as the decoy message when the attacker guessed the incorrect password. Some research implements the HE into an authentication scheme. However, the authentication scheme using HE has some weaknesses. The decoy message just uses one word and is still suspicious to the attacker. All of the decoy messages also have to be stored in the database. To address these problems, we proposed an authentication system that used honey sentences as the confirmation message instead of a word. Honey sentence is dynamically generated using natural language and has to be natural enough to fool the attacker. When the attacker inputs the incorrect password, the honey sentence is returned to the attacker, such that he could not determine the correctness of the guessed password. The experiment result showed that 80,67% of the generated sentences are considered natural, and the complexity of finding the correct password from all possible passwords is higher than the previous methods.
Smartphones Usefulness in Mobile Learning: The Moroccan University Case - Students of High School of Education and Training, Agadir

The development of digital technology has transformed our lifestyle and habits in all areas especially since the Covid-19 pandemic. Higher education is a field where digital pedagogy has impacted the practices of teachers and students on mobile devices (smartphones, tablets, etc.). Mobile learning has become one of the information and communication technologies in education (ICTE) used to search, communicate, study and disseminate information through several learning modalities. According to several studies, the latter is widely used among students. The aim of the present paper is to identify the use of mobile learning in the learning process of teacher training. More precisely, we question the contribution of smartphones in the learning process of students from the High School of Education and Training of Agadir. The data we have presented comes from a quantitative survey along with an interview-based research to analyze the effects of mobile technology. The research shows that students use their cell phones to search for educational resources, and also to interact with the instructors and exchange files with their peers, and they are highly motivated to have opted for this type of learning. Ideally, this article is intended to stimulate reflections on the subject of mobile learning and its integration in education in Morocco.
KinStyle: A Strong Baseline Photorealistic Kinship Face Synthesis with an Optimized StyleGAN Encoder

High-fidelity kinship face synthesis is a challenging task due to the limited amount of kinship data available for training and low-quality images. In addition, it is also hard to trace the genetic traits between parents and children from those low-quality training images. To address these issues, we leverage the pre-trained state-of-the-art face synthesis model, StyleGAN2, for kinship face synthesis. To handle large age, gender and other attribute variations between the parents and their children, we conduct a thorough study of its rich latent spaces and different encoder architectures for an optimized encoder design to repurpose StyleGAN2 for kinship face synthesis. The obtained latent representation from our developed encoder pipeline with stage-wise training strikes a better balance of editability and synthesis fidelity for identity preserving and attribute manipulations than other compared approaches. With extensive subjective, quantitative, and qualitative evaluations, the proposed approach consistently achieves better performance in terms of facial attribute heredity and image generation fidelity than other compared state-of-the-art methods. This demonstrates the effectiveness of the proposed approach which can yield promising and satisfactory kinship face synthesis using only a single and straightforward encoder architecture.
Fuzzy Rules Reduction Based on the Least Angle Regression Algorithm

High-dimensional data bring ‘Rule Explosion’. How to reduce the fuzzy rules becomes a novel research hotspot. In this paper, we proposed a rules-reduction model. In this proposed model we employ the idea of the sparse reconstruction algorithm to reduce the redundant rule, and transform the fuzzy rule reduction problem into the optimum solution of sparse reconstruction algorithms. We first compute fuzzy basis functions in order to represent the output signal. The expression function of the outputs signal can be seen as the sparse representation function with unknown parameters. Least angle regression algorithm is employed as an optimal method to solve the sparse reconstruction model. In the last section we use the proposed model to model time serial data, our algorithm performs better than Genfis2 and Genfis3.
A Novel Implementation of an Autonomous Human Following Drone using Local Context

This paper presents a novel implementation of an autonomous human following done using local context algorithm. The vision system consists of a drone equipped with a companion computer and camera. Through the vision system, drones can behave according to its desired application. In this study, the Pixhawk 2 Cube flight controller of the drone is connected to Odroid XU4, the companion computer. The OpenMV camera is connected to the Odroid XU4 and streams image bytes whereas the Odroid XU4 performs image processing to detect the human through local context. The Odroid XU4 controls the drone by sending commands to the flight controller based on detected objects. Drone following is implemented by detecting humans from the image stream provided by OpenMV and maintaining the detected human on the center of the image and within a specified distance through drone movements. The results show the single movements right, left, forward, and backward yielded low steady-state errors with settling time of about 3 to 4 seconds.
Handwritten Character Recognition Using Deep Learning (Convolutional Neural Network)

Handwriting recognition is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices.The purpose of the research is to design develops, construct, deploy, and test a convolutional neural network (CNN) for handwriting recognition.The CNN for handwriting recognition was developed with Python and MNIST dataset was used.CNN was evaluated together with Simple Neural Network (SNN) and it was found that a CNN operating on well-tuned hardware with GPU and adequate training data can recognize numbers with an accuracy of up to 98.7 percent.The accuracy and speed of the model can be improved by expanding the dataset, increasing the number of epoch runs, and executing it on parallel hardware.Using these strategies, an accuracy of up to 99.89 percent, can be achieved.
Performance analysis of classification of handwritten Devanagari digits using new augmentation technique of squared patterns and DCNN model

Handwritten script recognition has risen as a significant issue in a previous couple of years. Areas of computer vision and pattern recognition has attracted many researchers for a lot of real-time applications. Among these fields, Devanagari script recognition is a hot area for many Indian researchers due to its many applications like document reading, postal address sorting, etc. Lot of deep convolutional neural network (DCNN) models are being implemented to increase recognition accuracy. The objective of this study is to compare the performance of the proposed DCNN model by applying two image formats that are without processing (original) and with our proposed augmentation technique. The results show that the classification accuracy has increased by using the proposed technique of augmentation on a very limited training benchmark dataset of handwritten Devanagari digits proposed by Dongre and Mankar. The authors have achieved the highest classification accuracy of 98.49%.
Adaptive Proof of Work Architecture Design by Implementing Multiple Mempool

Several studies have been carried out to improve the blockchain’s Proof of Work (PoW) consensus method because it weakens its consensus latency. This is in addition to the introduction of consensus mechanisms, ranging from Proof of Stake (PoS) to Flexible Proof of Activity (PoA). However, these mechanisms are unable to increase latency significantly. In this study, an adaptive PoW framework design was developed by applying multiple mempool, which impact differences in the transaction speed of each transaction. The proposed method can facilitate different levels of consensus difficulty in each transaction. The difficulty level adjustment in PoW is based on collected and processed transaction data to obtain the level of importance in each transaction. The difficulty level in baseline PoW is used as the maximum level of the developed adaptive process, followed by their definition. Finally, higher difficulty levels are assigned to more critical transactions and vice versa. Therefore, the latency will increase as the consensus difficulty level rises and vice versa. Initial simulation results showed that when tested with 6,000 transaction data, the adaptive PoW framework design can increase the average number of transactions per second by up to 400%.
A Survey of Digital Watermarking Techniques for Data Protection in Cloud Computing

Recently digital watermarking techniques played an essential role in protecting and authenticating the copyright of multimedia content in a cloud. Based on the literature, there are several digital watermarking techniques used for data protection in cloud computing. However, each of these techniques has its own limitations such as high levels of piracy, theft, and unauthorized distribution of multimedia content. This Survey employs a content-based analysis approach to investigate the watermarking techniques that are more secure, imperceptible, and robust against various kinds of multimedia attacks. The survey shows that the hybridization of watermarking techniques and feature descriptors is more efficient in comparison to a single watermarking technique. This research work concludes that the hybridization technique and use of descriptors are more secure.
The ScalaFix Equation Solver

We present ScalaFix, a modular library for solving equation systems by iterative methods. ScalaFix implements several solvers, involving iteration strategies from plain Kleene’s iteration to more complex ones based on a hierarchical ordering of the unknowns. It works with finite and infinite equation systems and supports widening, narrowing and warrowing operators. It also allows intertwining ascending and descending chains and other advanced techniques such as localized widening.
Methodological approaches to the study of the problem of applying pedagogical diagnostics of the moral and psychological state of personnel

This article proposes a pedagogical analysis of the application of methodological approaches in the study of the moral and psychological state of personnel. The essence of pedagogical diagnostics of the moral and psychological state, as well as the ideological and value orientations of the worldview are revealed. The potential of methodological approaches in relation to pedagogical diagnostics of the moral and psychological state of personnel is considered. Definitions of the concept and essence of the axiological, personal and competency-based approaches are given. The values that the citizens of Kazakhstan, including the military personnel of the Border Service of the National Security Committee of the Republic of Kazakhstan must have, are disclosed in more detail. In the course of revealing the personal approach in the pedagogical process, attention is paid to the concept of personality, which requires the leader to know the individual characteristics and moral and business qualities of military personnel. In the course of studying the competency-based approach within the framework of the moral and psychological support of service and combat activities of the Border Service of Kazakhstan, competencies are revealed that characterize a person as a subject of vital activity. Focus of methodological approaches onto pedagogical diagnostics of the moral and psychological state of personnel is used.
A Security Model for Integration of Mobile Cloud Computing and Internet of Things

As new technology developments, the usage of cloud computing and mobile have hastily increased. In alternative words, mobile and cloud computing are most important in our future lives. It's an advanced technology that will provide data storage, infrastructure, and processing the data in cloud server. Internet of things (IoT) is a recent technology, it's developing quickly in the field of wireless communications. The target of the collaboration between IoT and mobile cloud computing (MCC) is to collect data using IoT and stored within the cloud. The authentication of data is very important to the integration of this technology. Hence, the authors introduced integrating of MCC and IoT, focused on the security problems. The extant the role of MCC to the IoT concludes this chapter. It shows the MCC technology increase and the functionality of the IoT. Finally, the authors propose a new secure model for mobile cloud and IoT integration.
Statistics

With collection metrics gathered, audited, and inventoried, statistics can be generated. Statistics such as collection use, age, turnover, and size tell collection managers a lot about how a collection is performing. They can make decisions throughout the collection life cycle based on these statistics. Library staff can also make decisions holistically about other library services based on collection statistics, such as staffing levels, event planning around popular topics, and technology needs for accessing the collection. Diversity statistics can be analyzed to help create a collection that reflects the community it serves as well as provides windows to learn about different people, customs, worldviews, and lifestyles. Statistics show how a collection has changed over time and give direction toward future changes.
Search, identification, and curation of cell and gene therapy product regulations using augmented intelligent systems

Manually keeping up-to-date with regulations such as directives, guidance, laws, and ordinances related to cell and gene therapy is a labor-intensive process. We used machine learning (ML) algorithms to create an augmented intelligent system to optimize systematic screening of global regulations to improve efficiency and reduce overall labor and missed regulations.Combining Boolean logic and artificial intelligence (i.e., augmented intelligence) for the search process, ML algorithms were used to identify and suggest relevant cell and gene therapy regulations. Suggested regulations were delivered to a landing page for further subject matter expert (SME) tagging of words/phrases to provide system relevance on functional words. Ongoing learning from the repository regulations continued to increase system reliability and performance. The automated ability to train and retrain the system allows for continued refinement and improvement of system accuracy. Automated daily searches for applicable regulations in global databases provide ongoing opportunities to update the repository.Compared to manual searching, which required 3-4 SMEs to review ~115 regulations, the current system performance, with continuous system learning, requires 1 full-time equivalent to process approximately 9,000 regulations/day. Currently, system performance has 86% overall accuracy, a recommend recall of 87%, and a reject recall of 84%. A conservative search strategy is intentionally used to permit SMEs to assess low-recommended regulations in order to prevent missing any applicable regulations.Compared to manual searches, our custom automated search system greatly improves the management of cell and gene therapy regulations and is efficient, cost effective, and accurate.
Web-Based Cooperation Information System at The Science Techno Park Technology Business Development Center

Science Techno Park (STP) Unand's partnership with various government, business, and legal institutions will continue for the foreseeable future in order to disseminate innovators' knowledge to the larger community. The issue that frequently arises is that the file search procedure is tough to locate. Furthermore, the enormous number of inventors at Andalas University are recorded in Microsoft Excel files, making it difficult for users to locate inventory data. The data that is then entered is frequently duplicated, leaving the user perplexed about the information presented. As a result, at STP Unand's Technology Business Development Center, a web-based cooperative information system was created. The goal is for STP Unand's engagement with partners to be automated, and for data collecting on the number of inventors in Unand to be structured. The SDLC (Software Development Life Cycle) method is used in this study. SDLC is a software development cycle that encompasses software development techniques, phases, and tools. The waterfall paradigm was used in the design of this application. the waterfall method was successfully used to build or design a collaborative information system application. Based on the outcomes of each stage of the waterfall method, namely analysis, design, coding, and system testing, it is possible to conclude that the design of this collaborative information system application has been successfully built in accordance with needs.
CHALLENGES OF SOLVING VISUALLY PRESENTED PROBLEMS

Organization of the teaching process which enables the acquisition of quality and effective mathematical knowledge applicable in different life situations and which lays the foundation for lifelong learning is based on problem-solving. Teaching how to solve visually presented problems is one objective that contributes to these overall goals. The main research goal of this paper is an experimental examination of the effects of problem-based teaching in the development of mathematical modeling skills involving visually presented problems. The effectiveness of teaching visually presented problems for the development of mathematical modeling skills in solving equations and inequalities in the fourth grade of primary school is examined. The descriptive method was used for the analysis, processing, and interpretation of the research results to investigate the type of errors pupils make when dealing with the visually presented information. Participants in the experimental program showed a higher level of knowledge when solving simple and complex equations and inequalities as well as in compos- ing texts based on given iconic representations of equations and inequalities, as opposed to the pupils not influenced by the experimental model. Given its positive effects on the development of mathematical modeling skills, teaching visually presented problems is justified during the early years of mathematics education.
Towards sparse matrix operations: graph database approach for power grid computation

The construction of new power systems presents higher requirements for the Power Internet of Things (PIoT) technology. The “source-grid-load-storage” architecture of a new power system requires PIoT to have a stronger multi- source heterogeneous data fusion ability. Native graph databases have great advantages in dealing with multi-source heterogeneous data, which make them suitable for an increasing number of analytical computing tasks. However, only few existing graph database products have native support for matrix operation-related interfaces or functions, resulting in low efficiency when handling matrix calculations that are commonly encountered in power grids. In this paper, the matrix computation process is expressed by a strategy called graph description, which relies on the natural connection between the matrix and structure of the graph. Based on that, we implement matrix operations on graph database, including matrix multiplication, matrix decomposition, etc. Specifically, only the nodes relevant to the computation and their neighbors are concerned in the process, which prunes the influence of zero elements in the matrix and avoids useless iterations compared to the conventional matrix computation. Based on the graph description, a series of power grid computations can be implemented on graph database, which reduces redundant data import and export operations while leveraging the parallel computing capability of graph database. It promotes the efficiency of PIoT when handling multi-source heterogeneous data. An comprehensive experimental study over two different scale power system datasets compares the proposed method with Python and MATLAB baselines. The results reveal the superior performance of our proposed method in both power flow and N-1 contingency computations.
Mining Metrical Data

Since its inception in the early ’90s, the StressTyp database (Goedemans, van der Hulst and Visch 1996) has been a valuable typological source for the testing of metrical theories against actual stress phenomena we find in the languages of the world. Grounded in the Primary Accent First-theory (van der Hulst 1984, 1996, 1997) we drew various quantitative analyses from the database to shed new light on metrical dependencies and metrical theory in general (e.g. Goedemans 2010a; 2010b; Goedemans and van der Hulst 2014). Since then, data from StressTyp has found its way to large-scale typological enterprises such as WALS (Haspelmath et. al. 2005) and the Typological Database System (Dimitriadis et al. 2009), but a marriage between StressTyp and the new methods and techniques that have become available since the dawn of the Digital Humanities has yet to take place. In this article we wish to present some exploratory work in this direction, in particular with respect to the continuum between left- and right-oriented stress systems we introduced for Arnhem Land languages in van der Hulst, Goedemans & Rice (2017). For this continuum, we would like to propose a computationally viable “distance scale” and visualize all the languages in StressTyp on that scale in an insightful way.