Reviewerly/exHarmony huggingface dataset:
A Research on the Online and Offline Blended Teaching Mode of College English Based on the Output-Oriented Method

Under the background and premise of higher education reform, the output-oriented method has established a scientific teaching concept for college English teaching and applied the integrated teaching ideology of learning and application to college English teaching, so as to improve the teaching effect. Based on the output-oriented teaching method, this paper analyzes and studies the online and offline blended teaching mode.
Quantitative and Qualitative Assessment of Indoor Exploration Algorithms for Autonomous UAVs

Indoor exploration is an important task in disaster relief, emergency response scenarios, and Search And Rescue (SAR) missions. Unmanned Aerial Vehicle (UAV) systems can aid first responders by maneuvering autonomously in areas inside buildings dangerous for humans to traverse, exploring the interior, and providing an accurate and reliable indoor map before the emergency response team takes action. Due to the challenging conditions in such scenarios and the inherent battery limitations and time constraints, we investigate 2D autonomous exploration strategies (e.g., based on 2D LiDAR) for mapping 3D indoor environments. First, we introduce a battery consumption model to consider the battery life aspect for the first time as a critical factor for evaluating the flight endurance of exploration strategies. Second, we perform extensive simulation experiments in diverse indoor environments using various state-of-the-art 2D LiDAR-based exploration strategies. We evaluate our findings in terms of various quantitative and qualitative performance indicators, suggesting that these strategies behave differently depending on the complexity of the environment and initial conditions, e.g., the entry point.
Comparative Analysis of Schnorr Digital Signature and ECDSA for Efficiency using Private Ethereum Network

This paper reports the use of the Schnorr Digital Signature as a new alternative instead of the Elliptic Curve Digital Signature Algorithm (ECDSA) currently used in Ethereum. Elliptic curve cryptography, which is the cryptographic background of blockchain digital signature, is explained in the background and related works. This study then examined, the problems facing ECDSA and how the Schnorr Digital Signature could solve them. The test was performed by comparing Schnorr and ECDSA implemented in a private Ethereum network environment. The performance results showed that Schnorr Digital Signature has strengths in terms of efficiency over ECDSA. The efficiency deals with the gas price, signature size, block size, block elapsed time, and signature algorithm calculation time.
A Simplifying-Matrix Method to Reduce the Storage Complexity in Studying Finite State Machines in the Framework of STP

The storage complexity problem is one of the severe challenges faced by research of finite state machines (FSMs) in the framework of STP, which must be overcome to make theoretical methodologies applicable to real applications. In this brief we propose a simplifying-matrix method to reduce the complexity of storing the dynamic matrix of an FSM when using computers to deal with problems. This method consists of three key components. Firstly, a mapping from logical matrices to logical vectors is proposed to retain effective information and remove large quantities of useless information from dynamic matrices of FSMs. Secondly, an alternative simplified dynamic matrix is constructed. Finally, a mapping from scalars to logical vectors is proposed to optimize the original model of FSMs and preserve the bilinear feature. In addition, we show that the proposed method is also applicable to reduction of the storage complexity of various logic dynamic systems in the framework of STP, such as, game systems, Boolean control networks, fuzzy systems and graphs.
A Novel Multi-Scale Transformer for Object Detection in Aerial Scenes

Deep learning has promoted the research of object detection in aerial scenes. However, most of the existing networks are limited by the large-scale variation of objects and the confusion of category features. To overcome these limitations, this paper proposes a novel aerial object detection framework called DFCformer. DFCformer is mainly composed of three parts: the backbone network DMViT, which introduces deformation patch embedding and multi-scale adaptive self-attention to capture sufficient features of the objects; FRGC guides feature interaction layer by layer to break the barriers between feature layers and improve the information discrimination and processing ability of multi-scale critical features; CAIM adopts an attention mechanism to fuse multi-scale features to perform hierarchical reasoning on the relationship between different levels and fully utilize the complementary information in multi-scale features. Extensive experiments have been conducted on the FAIR1M dataset, and DFCformer shows its advantages by achieving the highest scores with stronger scene adaptability.
Cybersecurity in South Africa

States are developing technologies to combat cyberthreats but the dilemma remains in balancing human rights and national security. Public consensus was based on how the securitizing agent propagated cyberspace as a threat to “national security and identity” in media and policy. To combat this threat state sovereignty is extended to cyberspace (cybercontrol) whereby the 2018 Anti-Cybercrime Law (ACL), equates vaguely defined cyber-“crimes” to “state security” threats. This chapter analyses Egypt’s cyberpolicy and the ACL, and examines the different online surveillance tools and policing measures. In light of the flawed governance apparent in drafting the ACL, the main question is what are the implications of excessive cyberspace securitization on digital rights and democracy? Sovereignty as a cybersecurity concern was introduced in the “Arab Convention on Information Technology Offences.” Protesters who raided the SSI building in March 2011 were able to disclose meeting minutes on tenders for Internet circumvention tools and spyware.
Pre-processing Techniques for Offline Tamil Handwritten Character Recognition

Handwritten Character Recognition (HWCR) is one of the difficult tasks in the field of pattern recognition and machine learning. In the HWCR application, human handwritten characters are recognized by the computer. Moreover, this can also be utilized in other applications such as postal processing script recognition, banking security, and scripting language identification. In the Handwritten Character recognition, the preprocessing phase has a great significance to improve the character recognition accuracy. In this paper, various preprocessing techniques, used for offline handwritten character recognition are discussed. Additionally, the various challenges concerning offline handwritten recognition are also addressed in this paper. The primary objective of this is to explain the significance of preprocessing techniques for offline HWCR in Tamil script.
Automated Machine Learning on High Dimensional Big Data for Prediction Tasks

Machine learning techniques have become deeply embedded in our daily lives. Artificial intelligence (AI) is a term used to describe the development and creation of “thinking machines” capable of emulating, learning, and replacing human intelligence. Machine Learning is one of the subsets of AI that has recently made major strides in enhancing real-time application processes and efficiency. The rapid growth of ML applications, as well as the field’s rapid expansion, has created a huge demand for ML methods that can be employed quickly and do not require any expert knowledge. However, many data scientists in charge of handling high-dimensional big data spend 60% of their time cleaning data and processing it with machine learning algorithms. AutoML is a field of study that tries to make machine learning more automated over time by using optimization and machine learning ideas. Automated machine learning (AutoML) is emerging as a promising approach for developing an AI system without the need for human intervention, and an increasing number of academics are focusing on AutoML. This paper at first presents an overview of AutoML. Secondly, the custom hybrid algorithm of the AutoML pipeline and its steps are discussed. Then, a comparison and experimental results of various AutoML tools for high-dimensional data have been illustrated. Finally, the open challenges and prospective research initiatives have been explained.
Supervision for Obscure Places to Avoid Intrusion using NNSA

The primary objective of this research work is to prevent humans from entering the secured places. The proposed method is based on the Nearest Neighbor Searching Algorithm (NNSA) and SMTP Protocol. When the motion is detected, the captured image is sent to the authorized person through GCM technology, and an alarm is triggered to inform the surrounding neighbors. Finally, in order to avoid intrusion, the proposed solution provides the graphical images to the authorized user as well as neighbors.
Comparative Analysis of CNN-Based Frameworks for Handwritten Arabic Numerals Recognition

The Arabic Language is the broadest and most used Language in the Arab World, with more than 423 million people. There are various representations in the Arabic Number system like Hindu-Arabic, Mashriqi Arabic, Eastern Arabic, and Western Arabic, which are look-a-like. Handwritten Character Recognition or Optical Character Recognition has gained popularity in recent days. This paper focuses on the importance of Optical Character Recognition (OCR) and different CNN-Based frameworks such as Lenet-5, ResNet-101, and ResNet-152 available today for OCR. This research study has created a dataset with more than 50,000 training and 7000 testing images of Arabic numbers ranging from 1 to 100 numbers written by various age groups and named it LBREADB. After that, various CNN-Based frameworks were trained with LBREADB and tested on the random inputs. A comparison has been made among the results based on the measurement criteria such as accuracy and prediction rate, which were shown at the ending.
Multimodal Registration of Optical Images and Digital Elevation Models Using Terrain Features

Registration of multimodal images such as optical data and digital elevation models (DEM) is a challenging task due to significant non-linear differences between these images. To address the problem, this paper proposes a new robust approach to integrate optical images and DEMs by using terrain features. In order to detect these features, a simulated image is generated based on the DEM by illuminating it in the geometry of the optical image acquisition, such that typical textures induced by topography are well present as those in the optical image. Hence, the multimodal registration is performed on the optical data and the simulated image to maximize the accuracy of feature matching. The Affine Scale-Invariant Feature Transform (ASIFT) is used to detect keypoints from the two images. Correspondences are matched through the nearest neighbor distance ratio, and outliers are removed using a two-step elimination procedure. The proposed method has been tested on five pairs of optical-DEM images with various spatial resolutions. Experimental results have shown that this method can provide robust registration for optical-DEM images with high accuracies of sub pixel level. The novelty of proposed method is to make use of terrain features for registration, providing a new perspective for the integration of multimodal images.
Playful strategies to enhance teaching-learning and the inclusion of children with Down syndrome

Down Syndrome is a genetic disorder characterized by the appearance of an extra chromosome in the human genome, discovered in 1866. This is one of the few genetic disorders where there are other chromosomes that are compatible with life. In Ecuador, Down syndrome occurs in 1 in 550 live births, an average much higher than the world rate of 1 in 700. The phenotypic characteristics that can occur in DS, there are 12 to 14 characteristics, but in each individual, it is presented between 6 to 8 the common ones are epicanthus, rounded head, short stature, coated and protruding tongue, wide and shorthands, psychomotor and mental-physical development is delayed, people with DS have a shorter average life than normal approximately 50 years, a large part of mortality is due to respiratory diseases, and cardiac malformation, the incidence of leukemia 20 times higher than the normal population. A systematic review is generated in the main repositories that cover the subject, using the PRISMA methodology to answer questions such as: What are the playful strategies used by children with Down Syndrome, What changes are generated by the use of technologies in people with DS, What aspects are should be considered for the correct motor stimulation of children with DS to contribute to motor development in an effective way.
An Adaptable Cognitive Microcontroller Node for Fitness Activity Recognition

The new generation of wireless technologies, fitness trackers, and devices with embedded sensors can have a big impact on healthcare systems and quality of life. Among the most crucial aspects to consider in these devices are the accuracy of the data produced and power consumption. Many of the events that can be monitored, while apparently simple, may not be easily detectable and recognizable by devices equipped with embedded sensors, especially on devices with low computing capabilities. It is well known that deep learning reduces the study of features that contribute to the recognition of the different target classes. In this work, we present a portable and battery-powered microcontroller-based device applicable to a wobble board. Wobble boards are low-cost equipment that can be used for sensorimotor training to avoid ankle injuries or as part of the rehabilitation process after an injury. The exercise recognition process was implemented through the use of cognitive techniques based on deep learning. To reduce power consumption, we add an adaptivity layer that dynamically manages the device’s hardware and software configuration to adapt it to the required operating mode at runtime. Our experimental results show that adjusting the node configuration to the workload at runtime can save up to 60% of the power consumed. On a custom dataset, our optimized and quantized neural network achieves an accuracy value greater than 97% for detecting some specific physical exercises on a wobble board.
A semantic based decision support framework to enable model and data integration

Model reusability and integration with datasets are major contributors towards their interoperability, the concepts that follows process established by computer aided process engineering (CAPE) community (Belaud & Pons 2002). This paper proposes a semantic approach which enables model/data registration, their discovery and concomitantly model their integration. The functionality of the process is fully controlled by a biorefining domain ontology implemented using Ontology Web Language (OWL) and tested using biorefining related scenarios.
Application of the Magnetic Geophysical Method to Exploration of the Potassic Zone in Some Porphyry Copper Deposits (Iran)

Haftcheshmeh, Masjeddaghi and Nowchun are three giant porphyry systems located in Iran. Because of the presence of potassic alteration outcrops in the Haftcheshmeh ore deposit (NW Iran), we decided to detect borders of this alteration zone by a ground magnetic method. Goal of exploration is to understand the distribution of concentration in porphyry systems, as the level of magnetization commonly differs across alteration boundaries. Further, 3D interpretation of magnetic data is important to improve our understanding of geology and alteration in the system. In this paper, qualitative and quantitative results of geophysical surveys are presented in the form of total field intensity, second derivative, upward continuation and pole reduction maps. Comparison of magnetic results with borehole logging provides a good pattern to a field geologist to assign and guide an exploration project. According to this pattern, all drill holes in Haftcheshmeh, Masjeddaghi and nowchun porphyry system were designed. According to the results for the mentioned copper systems, anomalous magnetic features are related to the sub-volcanic intrusions and high anomaly zones over the potassic alteration probably show the existence of magnetite. The potassic zone pattern can be used as a key in all further exploration of porphyry systems.
Examination of Possibility of Improving Accuracy on Predicting Photovoltaic Power Generation in Snowy Areas Using Panel Images

After the sequential end of feed-in tariff (FIT), the electricity generated by photovoltaic (PV) power generation system must be consumed by consumers themselves. Since PV power generation volume is easily affected by weather conditions, it is often used in combination with a power storage device to provide a stable power supply using PV power generation system. However, in order to efficiently control the power storage device according to the amount of power supplied by PV power generation system, it is necessary to accurately predict PV power generation volume. In this research, we will propose a method for predicting PV power generation volume by grasping the snow cover condition of the panel based on the panel image taken by the surveillance camera. We will compare and evaluate the prediction of PV power generation using the snow cover predicted from the panel image with related research and confirm the effectiveness of the proposed method.
Introduction to Python and Digital Image Processing

In the last chapter, we explored the amazing world of single-board computers and Raspberry Pi. We booted up the Raspberry Pi, connected it to the internet, and updated the Raspberry Pi OS.
Analysis and Categorization of A Village Education Community Operating System

The aims of this study are to derive the characteristics of a village education community operating system and to categorize its type through analyzing the system of City “A”. In order to achieve the purpose of this study, the village education community operating system of City A is analyzed, and the characteristics of the operating system are drawn by applying an analysis framework utilizing Hoy and Miskel's school social system model for the village education community operating system. As a result, the characteristics of the operating system of the village education community in City A are derived as environmental characteristics and operational characteristics, and is categorized based on the derived characteristics from the Pulppuri-Network-Modification. The action plans are as follows: firstly, it is suggested that sharing information is preceded to lay a foundation for the village education community. Secondly, personal motivation, learning culture, consultative group, and leadership must be harmonized in order to continue the operating system. Thirdly, it is important to focus on the interaction of the operating system within the relationship, rather than quantifying the characteristics of the region. This study is significant in that it recognizes the importance of the local environmental characteristics for the operation of the village education community and suggests implications for the operation of the village education community.
Expert Iteration for Risk

Risk is a complex strategy game that may be easier to understand for humans than chess but harder to deal with for computers. The main reasons are the stochastic nature of battles and the different decisions that must be coordinated within turns. Our goal is to create an artificial intelligence able to play the game without human knowledge using the Expert Iteration [1] framework. We use graph neural networks [13, 15, 22, 30] to learn the policies for the different decisions and the value estimation. Experiments on a synthetic board show that with this framework the model can rapidly learn a good country drafting policy, while the main game phases remain a challenge.
Learning Attribute-guided Fashion Similarity with Spatial and Channel Attention

Fashion image retrieval is one of the important services of e-commerce platforms, and it is also the basis of various fashion-related AI applications. Studies have shown that in a multi-modal environment (images + attribute labels), embedding items into specific attribute spaces can support more fine-grained similarity measures, which is especially suitable for fashion retrieval tasks. In this paper, we propose an attention-based attribute-guided similarity learning network (AttnFashion) for fashion image retrieval. The core of this network is an attribute-guided spatial attention module and an attribute-guided channel attention module, which correspond to the mapping between attributes and image regions, and the mapping between attributes and high-level image semantics, respectively. To make these two modules interact deeply, we design a parallel structure that allows them to share attribute embeddings and guide each other to extract specific features, which also helps to reduce the network parameters of the attention modules. An adaptive feature fusion strategy is proposed to synthesise the features extracted by the two modules. Extensive experiments show that the proposed AttnFashion performs better than current competitive networks in the field of fine-grained attribute-based fashion retrieval.
Robust one-dimensional phase unwrapping algorithm based on LSTM network with reduced parameter number

For interferometric optical fiber sensor applications, one-dimensional (1D) phase unwrapping is one of the most intractable steps. It is generally more vulnerable to noise impact compared with two-dimensional (2D) phase unwrapping, since the data correlation for the 1D phase unwrapping is very limited. In a recent work, we proposed the 1D phase unwrapping algorithm by combining quasi-Gramian matrix and deep convolutional neural network (DCNN). The obtained result is very robust and can work very stably under low signal-to-noise ratio (SNR) level. But the required parameter amount is large being about 10<sup>7</sup> making itself inadequate for resource limited computation platforms. In this work, a lightweight phase unwrapping algorithm based on long short-term memory (LSTM) network is proposed, which utilizes only 10<sup>6</sup> parameter amounts, being 1 order smaller than the DCNN. Simulation results demonstrate that in the SNR range of 0 to 16 dB, the LSTM-based method shows comparable or better performance compared with the DCNN method, and for the very low SNR of -2 dB, only small performance decrease is observed. The generalization ability of the proposed method is also verified by using the experimental data collected from an actual phase-sensitive optical time domain reflectometry (&#x03D5;-OTDR) system.
Private Information Delivery with Coded Storage

In private information delivery (PID) problem, there are K messages stored across N servers, each capable of storing M messages and a user. Servers want to convey one of the K messages to the user without revealing the identity (index) of the message conveyed. The capacity of PID problem is defined as the maximum number of bits of the desired message that can be conveyed privately, per bit of total communication, to the user. For the restricted case of replicated systems, where coded messages or splitting one message into several servers is not allowed, the capacity of PID has been characterized by Hua Sun in "Private Information Delivery, IEEE Transactions on Information Theory, December 2020" in terms of K, N and M. In this paper, we study the problem of PID with coded storage at the servers. For a class of problems called bi-regular PID we characterize the capacity for N = K/M and for N > K/M we provide achievable scheme. In both the cases, the rates achieved are more than the rates achievable with the replicated systems.