Reviewerly/exHarmony huggingface dataset:
Comparison of Deep Learning-Based Semantic Segmentation Models for Unmanned Aerial Vehicle Images

Unmanned aerial vehicles (UAVs) have been used for a variety of tasks, including transporting food, medical supplies, packages, and other items. Semantic segmentation allows us to understand urban scenes, which is important for improving the safety of autonomous UAVs. In this paper, we investigated several deep learning-based models for segmentation in aerial images. The models are built based on FeN, U-Net, and DeepLab architectures. We trained and evaluated models using a publicly available dataset. The experimental results show that all models have high potential with a small number of training samples. We also compared the results and provided possible suggestions for further work.
Efficient VLSI Architecture of Bluestein’s FFT for Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) is a powerful scheme that allows computations to be performed on encrypted data. To reduce the computational complexity, double-CRT representation has been adopted in the BGV-FHE cryptosystem, in which the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> -CRT, also known as the polynomial-CRT, can be viewed as performing Discrete Fourier Transform (DFT). Since the point size of the DFT is usually non power of two, the traditional Cooley-Tukey FFT algorithm cannot be directly applied to reduce the complexity. This paper explores efficient VLSI architecture of Bluestein’s FFT for BGV-FHE applications. A mixed-radix single-port merged-bank memory addressing algorithm is presented to increase the effective memory bandwidth and to reduce the required memory area concurrently. The evaluation was conducted by implementing a Bluestein’s FFT compiler that can be configured to generate different point sizes of DFT designs for BGV-FHE. Analytical results also show that the proposed Bluestein’s FFT design can lead to a more area-efficient solution as compared to the mixed-radix counterpart in general cases.
Synthesizing Talking Face Videos with a Spatial Attention Mechanism

Recently, talking face generation has drawn considerable attention of researchers due to its wide applications. The lip synchronization accuracy and visual quality of the generated target speaker are very crucial for synthesizing photo-realistic talking face videos. Prior methods usually obtained unnatural and incongruous results. Or the generated ones comparatively has high fidelity, but only for a specific target speaker. In this paper, we propose a novel adversarial learning framework for talking face generation of arbitrary target speakers. To sufficiently provide visual information about the lip region in the video synthesis process, we introduce a spatial attention mechanism enabling our model to pay more attention to the lip region construction. In addition, we employ a content loss and a total variation regularization for our objective function in order to reduce lip shaking and artifacts in the deformed regions. Extensive experiments demonstrate that our method outperforms other representative approaches.
PENERAPAN METODE SAS UNTUK MENINGKATKAN KETERAMPILAN MENULIS PERMULAAN SISWA KELAS II SD NEGERI NGADIMERTO

This study aims to describe: (1) the application of the SAS method, (2) the students' interest in the application of the SAS method, (3) the improvement of pre-writing skills using the SAS method. This research includes Classroom Action Research (PTK) with the Synthetic Structural Analytical (SAS) method. This research was conducted at SD Negeri Ngadimerto Banyuurip with 7th grade students as research subjects. The instruments used were observation sheet guidelines, interview sheets, field notes, test question sheets, and data collection methods in the form of observation. From the research results, it is known that: (1) the application of the SAS method in this study was successfully carried out. This can be seen from the increasingly active students and the neat results of the students' writing; (2) the students' interest in learning Indonesian with the application of the SAS method has increased. In the pre-cycle stage, only two students are active in the class and the students' writing results cannot be said to be good. Cycle I, four students had started to show their confidence. The writing attitudes and results of the students' writing are also getting better. In cycle II, five students showed their self-confidence. Students are also used to writing carefully and with the right attitude; (3) the results of early writing skills using the SAS method are improved. In the pre-cycle stage, the average results of students reached 63.56. Two students have reached KKM 70. In the first cycle stage, three students have reached the KKM with an average result of 67.97. Cycle II, five students had reached the KKM with an average result of 73.38.
Primary Mobile Image Analysis of Human Intestinal Worm Detection

One among a lot of public health concerns in rural and tropical areas is the human intestinal parasite. Traditionally, diagnosis of these parasites is by visual analysis of stool specimens, which is usually tedious and time-consuming. In this study, the authors combine techniques in the Laplacian pyramid, Gabor filter, and wavelet to build a feature vector for the discrimination of intestinal worm in a low-resolution image captured with mobile devices. The dimension of the feature vector is reduced using principal component analysis, and the resultant vector is considered as input to the SVM classifier. The proposed framework was applied to the Makerere intestinal dataset. At its preliminary stage, the results demonstrate satisfactory classification with an accuracy rate of 65.22% with possible extension in future work.
Higher Agricultural Education: Orienting Professional Tasks to Shape Sectoral Digital Competencies

The relevance of the research topic is determined by the need to provide the agricultural sector of the Republic of Buryatia with highly qualified personnel. The paper aims to assess the possibility of forming professional competencies of students, considering the specifics of agricultural production and the trends in the digital development of the industry. The paper uses modeling methods in practice. The results of the analysis of the applied Federal State Educational Standard of Higher Education and professional standards of activity (in terms of requirements for the formation of students’ digital knowledge, skills, and abilities) indicate the mutual inconsistency of the content of these requirements, which makes it challenging to adapt educational programs and set professional tasks with their focus on the formation of digital competencies for work in today’s agriculture. The authors conclude that it is necessary to adapt educational programs to the requirements of the digital economy in the agricultural sector. This will actualize the content of educational programs of higher education of higher agricultural education institutions, considering the employers’ needs in specialists with competencies in various areas of agriculture and a set of digital knowledge, skills, and abilities applicable to the agricultural sector. Thus, the paper focuses on forming the professional competencies of students in higher agricultural education programs to ensure the digital development of agricultural organizations. The paper presents a rationale for approaches to adapting basic professional educational programs (BEP) to the digital economy, in which a special role is given to “Applied Computer Science,” “Economics,” and “Management.” The authors consider the possibilities of setting and orienting the content of professional tasks on the formation of digital competencies. The main aspects of the transformation of higher agricultural education in the conditions of Industry 4.0 are presented.
Building a Bilingual QA-system with ruGPT-3

In this work, we present an approach of cross-lingual transfer learning for English and Russian languages within the QA task. Our approach implies using a generative transformer model that has seen Wikipedia texts in both languages during the pretraining phase and then fine-tuning it with a special token of language, forcing the model to generate texts in a particular language. We are focusing on SQuAD data (English) and updated SberQuAD data (Russian) plus their translations for training and testing, and use ruGPT-3 XL model, which is forced to answer questions in English based on Russian paragraphs and reverse: can answer in Russian when provided information in English. Monolingual QA-abilities of the model are also preserved. Our results show that the fine-tuned model demonstrates bilingual ability and can generate answers that are close to correct in fuzzy metrics: model generates answers in Russian when based on English texts: 75% named entities ratio, 28% Levenshtein Distance string matching, 28% ROUGE-L; model generates answers in English when based on Russian data: 51% named entities ratio, 27% Levenshtein Distance string matching, 27% ROUGE-L; monolingual Russian quality: 83% named entities ratio, 59% Levenshtein Distance string matching, 57% ROUGE-L; monolingual English quality: 52% named entities ratio, 24% Levenshtein Distance string matching, 25% ROUGE-L.
Robust speech security under doubly selective fading channel

In this paper, the encrypted speech transmission model has been assumed through orthogonal frequency division multiplexing (OFDM) over doubly selective fading channel. Two stages of security are used to enhance the security of OFDM system that are ciphering and scrambling techniques. A two dimensional (2D) logistic map is used to provide pseudo random bit generator (PRBG) and index permutation that are needed in the ciphering and the scrambling techniques, respectively. The simulation of the proposed system is programmed using MATLAB program version 2019a for 64 QAM and 256 QAM modulation and different speed velocities of 200 km/h and 500 km/h using Hybrid ciphering and scrambling techniques. The simulation results show that the speech signal over OFDM system has good intelligibility measures and robust against any attack and the signal is decrypted with little effect on the bit error rate.
Access Control Mechanism for Edge Nodes

Aiming at the problems of poor security, difficult data access control and inflexible authorization process of edge nodes in the Internet of Things environment, an access control mechanism for edge nodes is proposed. This mechanism builds a blockchain-based IoT data security and trusted sharing framework, deploys smart contracts for dynamic access control. The dynamic token mechanism is used to restrict access to edge node resources, and a variable token value is set to periodically generate access credentials for user login. The user’s access information is recorded on the blockchain, the reputation is evaluated according to the access information stored on the chain, and the resource access level is dynamically updated on a regular basis. Finally, deploy IoT edge nodes and build a fabric-based dynamic access control framework to conduct simulation and comparison experiments., which proves the feasibility and security of the access control scheme for edge nodes.
PU_Bpub: High-Dimensional Data Release Mechanism Based on Spectral Clustering with Local Differential Privacy

Although the release and analysis of high-dimensional data bring tremendous value to people, it causes great hidden danger to participants’ privacy in the meantime. Various privacy protection methods based on differential privacy have been proposed at present. However, most of them cannot simultaneously solve the problems of high computational overhead and privacy threats from untrusted servers caused by the curse of high dimensionality. Therefore, we propose a safer and more effective high-dimensional data release algorithm based on local differential privacy, which is referred to as PU_Bpub. It effectively preserves the dimensional correlation of the original high-dimensional data and reduces the communication overhead of synthetic data. Extensive experiments on real-world datasets demonstrate that our solution substantially outperforms the state-of-the-art techniques in terms of computational overhead, and the synthetic dataset has high utility.
Domain Knowledge Driven Semantic Communication for Image Transmission Over Wireless Channels

Recent progress in semantic communication has shown potential in improving transmission efficiency by the removal of signal-level redundancies. However, existing semantic communication systems fail to consider the high-level conceptual semantics embedded in the source, which is critical for human perception especially in certain domains. This letter proposes a novel domain knowledge driven semantic communication (DKSC) system, which is a dual-path framework with semantic extraction and reconstruction at both information and concept levels. Specifically, the dual-path semantic encoder and decoder are jointly designed and optimized in the end-to-end wireless communication system based on deep neural networks. To illustrate the performance of DKSC system, we execute image transmission task over wireless channels in medical domain as an example, and the impacts of various channel models and compression ratios are investigated. Experimental results demonstrate that with the integration of domain knowledge semantics, the proposed DKSC system outperforms other baseline schemes in terms of image restoration metrics with robustness against channel variations.
Multi-Modal Reciprocal Spatiotemporal Framework for Predicting Usage Trend of Knowledge Services

As an emerging concept, Knowledge as a Service (KaaS) aims to provide on-demand content-based (data, information, knowledge) delivery to meet the needs of users. With the prosperity of knowledge services, the prediction of the usage tendency of knowledge services has become an important and timely research topic. This study focuses on speculating the possible popularity of knowledge services in the next period of time, which can assist other downstream service tasks such as service recommendations. The interactions among knowledge services and their rich information (such as historical usage observation and text information) provide grounding for predicting the usage trend of services. However, recent spatial-temporal prediction based on graph neural networks usually depends heavily on the quality of manually created graphs, which may be expensive for knowledge services. To tackle such a limitation, this article proposes a novel Multi-modal Reciprocal SpatioTemporal (MRST) framework, which can jointly mine spatial dependencies and model time patterns for spatiotemporal coupling prediction. Two types of Edge Inference Networks (called EIN-o and EIN-t) are designed to sufficiently discover the spatial dependencies among knowledge services based on the data of usage observation sequences and service descriptions, respectively, and generate multi-modal directed weighted knowledge service graphs. Based on these graphs, MRST integrates GCN-based spatiotemporal prediction models as backbones to make predictions. Particularly, MRST features a unique reciprocal framework. On the one hand, EINs infer and generate multi-modal graphs to serve GCNs; on the other hand, GCNs utilize such spatial dependencies to make predictions and then introduce feedback to optimize EINs. In the meantime, to facilitate reproducible research, we collect a new knowledge service dataset from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Wikipedia</i> called Wiki-EN dataset. Experiments on this real data set show that the proposed MRST framework significantly surpasses the baselines and can learn meaningful spatial dependencies outside the predefined graphic structure.
Political Economy of the "Virtual" Internationalisation of Higher Education to Foster Glocal Sustainable Development

Background: The COVID-19 pandemic challenged the higher education institution’s face-to-face education. Higher education institutions have overcome this challenge through enhanced virtual education which has provided further opportunities to the higher education institutions. One of these opportunities is the ‘virtual’ internationalisation of higher education which enables higher education institutions to reach more students globally. &#x0D; Purposes: This paper aims to investigate the ‘virtual’ internationalisation of higher education’s role in glocal sustainable development and how to enhance its use to support glocal sustainability and sustainable development. This paper emphasises importance of political economy of the ‘virtual’ internationalisation of higher education to support glocal sustainable development and environmental policies.&#x0D; Methodology/Approach: The aim of this paper is achieved based on an in-depth literature review.&#x0D; Findings: This paper highlights effective, strategic and successful ‘virtual’ internationalisation of higher education’s role in competitiveness of higher education institutions. This paper highlights political economy of the ‘virtual’ internationalisation of higher education and provides recommendations and key success factors for the ‘virtual’ internationalisation of higher education to enhance glocal sustainable development and sustainability as well as environmental policies. This paper emphasises importance of considering the ‘virtual’ internationalisation of higher education in countries’ sustainable development plans, strategies and policies. &#x0D; Discussion: Effective and strategic ‘virtual’ internationalisation of higher education can support higher education institutions’ competitive advantage globally. They can support higher education institutions’ success in getting intelligent students from all over the world. This can further contribute to their competitiveness. Furthermore, this can enable them to employ, in these ‘virtual’ internationalisation of higher education programmes, globally competitive and competent academic staff from all over the world. This paper can be useful to academics, policy-makers and researchers in the relevant field.
Enhanced Security In Medical Image Steganography -A Hybrid Approach Using Spatial And Transform Domain

Image steganography is an essential method in the subject of information hiding, which is the art of concealing confidential data within a regular image to prevent tampering. There is a risk of tampering with medical images when they are transmitted over an insecure public network, which makes it difficult for diagnosis. As a result, it's critical to ensure the integrity of these images to avoid any unwanted changes. In this paper, we use the SHA256 technique to calculate the hash value of ROI to ensure medical image integrity. DCT and IDCT will be used to embed the hash in the RONI. We may check the image's authenticity by evaluating the hash value at the receiver end. The hash function does not match if there is any tampering. This study presents a hybrid approach to enhancing security. The altered image is encased in a regular-looking image. It aids in the concealment of confidential data. This multi-pronged approach will improve security.
Label Enhancement Using Inter-example Correlation Information

Label distribution learning (LDL) to characterize the importance of different labels by label distribution has achieved good results in many application fields. LDL can learn more semantic information from the data than multi-label learning, however, most of the data in practical applications are single-label annotated or multi-label annotated, lacking the complete label distribution information suitable for label distribution learning. Thus, label enhancement (LE) is proposed to recover the label distributions from the logical labels. In this paper, we propose a new label enhancement method using inter-example correlation information that can automatically learn label correlations from data and jointly learn model and label correlations in a unified learning framework. Moreover, we also exploit the feature correlations constraining the model in the proposed method, which solves the problem that existing label enhancement algorithms cannot fully utilize the label information to improve the model performance. The experimental results on several real-world data sets validate the effectiveness of our method.
A Comparison Study a Mong Optimization Methods for Solar PV Hybrid System

The maximum output power can be obtained from sun energy to supply grid tie system .The highest output power that leads to an improve in the efficiency of the solar system related to the weather conditions when the panel exposed solar radiation uniform and changed.In this paper a comparisison study for different optimization methods include P&amp;O ,PSO and GWO was made this study obserive which optimization methods gave the highst power . This means that the grey optimizations technologies lead to increase the power received from the PV that’s led to increase the efficiency
Adagio: A Bot for Audio Processing Against Violence

Within social networks, audio is a vehicle for violent, bullying or generally unwanted content. This research intends to propose an automatic tool for extracting text from an audio stream. Microsoft azure cognitive cloud services, and in particular, Speech SDK and bot SDK are used for extraction and recognition tasks. The extracted text can then be analyzed using techniques and algorithm known for analyzing critical situations such as violence or bullying but is applicable in any context.
An Efficient Hardware Architecture for DNN Training by Exploiting Triple Sparsity

Recently, on-device DNN training has attracted much attention due to its high performance on edge devices and great ability to protect user privacy. Low-power and high throughput implementations of DNN training are highly desired for resource-limited devices. In this paper, we present an efficient hardware accelerator that exploits triple sparsity to reduce the number of unnecessary operations during DNN training. The gradients pruning algorithm is employed to bring error sparsity. Firstly, sparse data are represented in compressed sparse block format, which is suitable for different memory access patterns in all training phases. Secondly, an efficient sparsity detection logic based on the aforementioned data storage format is proposed, which adopts a 2-level grained mechanism. Coarse-grained mask-matching units are reused to improve the energy efficiency, while fine-grained mask-matching units make PEs work independently to enhance throughput. Thirdly, based on the above sparsity detection logic, we propose an efficient architecture for DNN training. Experimental results show that our design can achieve up to 42.1 TOPS and 174.0 TOPS/W in terms of throughput and energy efficiency, respectively. The energy efficiency of our design is $2.12\times $ higher than the state-of-the-art training processor. For training a ResNet-50 model on the CIFAR10 dataset, the energy efficiency of our design achieves 14.10, 96.57, and 84.43 TOPS/W in the FP, BP, and WG phases, respectively.