Reviewerly/exHarmony huggingface dataset:
Perancangan Sistem Keamanan Pintar Kamera Night Vision Auto Color berbasis Raspberry PI

In the rapid development of technology, there is a high crime rate. Therefore becomes very important. Many efforts can be made to secure a place and room, such as installing a security lock on the door or using a CCTV camera. But this is not enough to prevent crime from happening. Night Vision is the ability to see either in a biological or technological sense in a dark environment. Night vision capability can be achieved using two approaches, namely increasing the visible spectrum of the wave spectrum or increasing the ability to see low light intensities. Night vision has advantages and disadvantages, especially in conditions of very low light intensity. This technology has developed from time to time such as, there is the camera that uses sophisticated and modern electronic systems. Therefore, in this study, modern technology was designed with a smart security system that can be used to maintain home security or hunt at night.
Trajectory Generation for Energy-Constrained Aerial Robots with Application in Area-Coverage

In this article, we address the trajectory generation for an energy-constrained quadrotor in applying for area coverage. The traditional path planning algorithm of area coverage finally generates waypoints for the robot to follow instead of a continuous path. Waypoints have limited ability in the quadrotor's future trajectory planning and cannot accurately describe the quadrotor's energy consumption process. Based on the above problems, firstly, VPG method is used to generate the waypoints sequence under the area coverage model. Secondly, we construct the energy constraint model which can maximize the path length with limited energy and waypoints are smoothed into continuous trajectory with more information by using B-splines. Finally, the trajectory in the form of B-splines is optimized to satisfy the dynamic constraints of the real quadrotor. The work of this paper has been verified on quadrotor flight experiments.
Fall Detection for the Elderly Based on Online Transfer Learning

Fall detection is of great significance to the elderly. The wearable fall detection device can collect real-time data to identify falling events, thereby helping to protect the elderly from suffering further injuries. The limited sensor data from older adults in the SisFall dataset is insufficient for training a fall detection classifier that is specifically tailored to older adults. This paper proposes a fall detection method based on online transfer learning. The method uses the weighted online sequential extreme learning machine with a forgetting factor as an online classifier, which can effectively update the model in real time based on continuously collected data, thereby improving the accuracy of fall detection among elder adults. By dynamically updating the combination weights of offline classifiers and online classifiers to transfer source domain knowledge to the target domain, the proposed method improves classification accuracy in the target domain. Moreover, we incorporate concept drift detection to adapt to changes in the data distribution over time. Experimental results show that the improved algorithm has a higher online accuracy.
A New Low-Bit Quantization Algorithm for Neural Networks

Model quantization is one of the hot research topics in the field of model compression, which is important for reducing the model size and memory overhead. The current stage of model quantization methods usually uses straight-through estimators(STE) to train the network, which avoids the zero-gradient problem by directly replacing the derivative of the rounding function with the derivative of the constant function, but STE updates the values of the weights, in the dequantization propagation phase only propagate the same gradient. The discrete error between input and output is not taken into account, which will cause a large accuracy loss. It is not only difficult to achieve accuracy comparable to the full precision model, but also not stable enough to converge for large datasets with a high number of classifications. To address the shortcomings of floating-point gradient update and accuracy loss of low-bit quantization, we propose a new low-bit neural network quantization (LEQ) method. LEQ jointly optimizes the discrete error problem between input and output in backpropagation and the model stability problem, reduces the accuracy loss of the model during training, and uses symmetric cross-entropy loss function to enhance the robustness of the model, which has obvious advantages on several large public datasets. The accuracy of TOP-l can be 79.9,88.6,92.2,92.6, 98.7,98.4,99.1, and 99.1 when quantified to INT2, INT3, INT4, and INT8 on CIFAR-I0 and MNIST datasets, respectively.
A Streamlined GAN Based Real-Time Image Information Hiding for Networked Visual Servo Systems

The existing image information hiding methods based tampering detection cannot satisfy real-time requirement of networked visual servo systems (NVSSs). To solve the problem, a novel streamlined generative adversarial network based real-time image information hiding (SGAN-RIIH) method is proposed by reducing the layer number of convolutional neural network in the generator and selecting random frame to carry out image information hiding, where the consumed time of SGAN-RIIH method is decreased importantly and the image hiding performance is still satisfactory. Specially, the generative adversarial network (GAN) trained by COCO datasets can be implemented to other datasets. The proposed SGAN-RIIH method is verified for tampering detection on NVSSs.The experimental results show that the SGAN-RIIH method can assist in tampering detection.
Learnable Spatial-Spectral Transform-Based Tensor Nuclear Norm for Multi-Dimensional Visual Data Recovery

Recently, transform-based tensor nuclear norm (TNN) methods have received increasing attention as a powerful tool for multi-dimensional visual data (color images, videos, and multispectral images, etc.) recovery. Especially, the redundant transform-based TNN achieves satisfactory recovery results, where the redundant transform along spectral mode can remarkably enhance the low-rankness of tensors. However, it suffers from expensive computational cost induced by the redundant transform. In this paper, we propose a learnable spatial-spectral transform-based TNN model for multi-dimensional visual data recovery, which not only enjoys better low-rankness capability but also allows us to design fast algorithms accompanying it. More specifically, we first project the large-scale original tensor to the small-scale intrinsic tensor via the learnable semi-orthogonal transforms along the spatial modes. Here, the semi-orthogonal transforms, serving as the key building block, can boost the spatial low-rankness and lead to a small-scale problem, which paves the way for designing fast algorithms. Secondly, to further boost the low-rankness, we apply the learnable redundant transform along the spectral mode to the small-scale intrinsic tensor. To tackle the proposed model, we apply an efficient proximal alternating minimization-based algorithm, which enjoys a theoretical convergence guarantee. Extensive experimental results on real-world data (color images, videos, and multispectral images) demonstrate that the proposed method outperforms state-of-the-art competitors in terms of evaluation metrics and running time.
MODEL FOR PEDAGOGICAL SUPPORT OF FORMING THE PROFESSIONAL FLEXIBILITY OF COMPANIES’ SENIOR EXECUTIVES

In today's environment, it is necessary for senior executives of companies to have professional flexibility when making strategic management decisions. To solve this problem, the author proposed to use specially organized additional professional education, which includes the use of media resources of TED Talks. The purpose of this article is to develop a model of pedagogical support to form the professional flexibility of senior executives of companies. The author proposes a model of pedagogical support to form the professional flexibility of senior executives of companies as a system, which functions in the frameworks of system, resource and synergetic approaches. The media-resources of TED Talks were selected as an effective method to form the professional flexibility of senior executives of companies, as TED Talks increase the motivation and involvement of senior executives of companies in forming professional flexibility. The obtained theoretical and practical results of the work can be applied in the programs of additional professional education, designed for training of senior executives of companies.
The Gene-Om of The Coming Civilization: The Genesis and The ABCs - Artificial-Intelligence (A), Block-Chain (B) and Crypto-Currencies (C)

Artificial-Intelligence (A), Block-Chain (B) and Crypto-Currencies (C) have to recognized as the true foundational elements or the ABCs - the alphabet - of the upcoming technological age. The combination of these ABCs will be the code that will write the future of human civilization. This coming combination of Artificial-Intelligence (A), Block-Chain (B) and Crypto-Currencies (C) will be built upon two concepts blockchain components termed: Genesis and Gene-Om. We develop a set of models and methodologies for the incubation of artificial intelligence in the decentralized domain. We provide incentive mechanisms - on blockchain systems using crypto-currencies - to ensure that the maximum number of participants - all of life - can benefit from the increasing use of Artificial Intelligence (also, AI) in our lives.
Preventing Data Threats using Cloud Computing with Block Chain Technology

Cloud services are susceptible to attacks because they're extensively available. In Cloud computing, a fairly new service that falls under the marquee of cloud services, data manipulation poses a significant trouble to data integrity. Cloud computing in a variety of operation disciplines want to be certain that the data they use is dependable and accurate. On the other end of the diapason, block chain is a digital tally that cannot be altered. It can be combined with Cloud technology to produce a secure Cloud computing terrain. For all homomorphic encryption styles, this paper proposes a system that combines block chain and cloud computing to guarantee data integrity. The proposed plan uses the intricate Fault Tolerance agreement to construct a distributed network of processing CSPs for ultimate authority over the data. After specific computations performed by all CSPs, they produce an expert hash an incitement for their information base. Master hash values are saved in Bit coin or Ethereum block chain networks to guarantee the product of inflexible data. The expert hash values can be gotten by following the block title address for check purposes. We discovered that Ethereum outperforms Bit coin in terms of online performance and lower customer fiscal costs. Also, we describe the proposed scheme's data security conditions, ground- position perpetration, and planned work.
Get the gist of the story: Neural map of topic keywords in multi-speaker environment

Neural representation of lexico-semantics in speech processing has been revealed in recent years. However, to date, how the brain makes sense of the higher-level semantic gist (topic keywords) of a continuous speech remains mysterious. Capitalizing on a generative probabilistic topic modelling algorithm on speech materials to which participants listened while their brain activities were recorded by Magnetoencephalography (MEG), here we show spatio-temporal neural representation of topic keywords in a multi-speaker environment where task-relevant (attended) and -irrelevant (unattended) speech co-exits. We report the difference of neural representation between salient and less salient semantic gist of both attended and unattended speech. Moreover, we show that greater sensitivity to semantically salient unattended speech in the left auditory and motor cortices negatively mediates attended speech comprehension.
Linguistic and discursive properties of hate speech and speech facilitating the expression of hatred

Abstract Examples of posts from three Finnish (236 posts) and one French (410 posts) discussion threads are analyzed with regard to lexicon, grammar, speech acts, and topoi. The aim is to illustrate the different means used to express hatred or speech pertaining to ideological and thematic spaces in which the expression of hatred may be legitimized. The examples represent everyday discourse, which is an influential arena for the materialization of ideologies. Given the lack of linguistic and discursive tools used exclusively to express or legitimize hatred, doing research on hate speech always entails taking a specific ideological stance: neither linguistics nor pragmatics or discourse studies can provide a definition of hate speech that is valid in all contexts. Triangulating different methodological and theoretical perspectives is necessary to produce convincing research results.
Leveraging Machine Learning to Assist Clinicians with Clinical Dementia Rating (CDR) Scores in Online Dementia Diagnosis

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The Role of the Special Teacher in the Formation of Motivation for Educational Activities of Students with Intellectual Disabilities

Motivation for learning activities of students with intellectual disabilities is one of the factors of their success and development. Especially relevant is the motivation of students during distance learning, which is due to their psychophysical features. The study shows that special teachers understand the importance of motivation in achieving academic performance of students with intellectual disabilities, forming their skills to overcome difficulties, and developing their personality. Students with intellectual disabilities are motivated to learn differently from their peers with typical development. These features require from the correctional teacher much greater efforts in stimulating students to assimilate knowledge and skills; constant support of cognitive interests of students, overcoming their passivity, developing skills of independence, forming of initiative. It is established that the role of the special teacher in the formation of motivation of educational activity of students with intellectual disabilities is decisive, in particular through such features of students as: low level of cognitive activity, inertia of nervous processes, lack of formation of logical connections. The prospects of scientific research are the development of an integral system of correctional and developmental work, which will ensure the formation of all types of motivation of educational activities for this category of students.
LexMeta model za leksičke resurse

This paper presents LexMeta, a metadata model for the description of lexical resources, such as dictionaries, word lists, glossaries, etc., to be used in language data catalogues mainly targeting the lexicographic and broader humanities communities but also users exploiting such resources in their research and applications. A comparative review of similar models is made in order to show the differences and commonalities with LexMeta. To enhance semantic interoperability and support the exchange of (meta)data across disciplinary and general catalogues, the most influential models for our purposes, namely FRBR (used in library catalogues) and META-SHARE (used for language resources), are selected as a base for the design of LexMeta. We discuss how these models are aligned and extended with new properties as required for the description of lexical resources. The formal representation of the model following the Linked Data paradigm aims to further enhance the semantic interoperability. The choice to implement it in two formats (as an RDF/OWL and as a Wikibase ontology) facilitates its adoption and hence its enrichment, yet poses challenges as to their synchronisation, which are addressed through automatic workflows. We conclude with ongoing and planned activities for the improvement of the model.
Peculiarities of using electronic learning tools in preschool educational organizations

The article is devoted to the study of the peculiarities of the use of electronic learning tools in preschool educational organizations in general, as well as the equipment and efficiency of the use of digital educational devices in preschool institutions of Ivanovo region in particular. By means of an electronic questionnaire of kindergarten managers, data on the technical means of education available in preschool organizations, as well as their number were obtained. The survey was attended by the heads of 181 preschool organizations of the Ivanovo region, which is about 49 % of the network of preschool educational organizations in the region. It was found that most of the existing electronic learning tools for preschoolers in kindergartens are absent or are available in a single number. Among the most common types of equipment were screen and projector, interactive whiteboard, and interactive sandbox. The article analyzes the devices used to work with preschoolers (3D-pen, tablet, educational robot, robotics kit, cartoon studio, etc.) from the perspective of the activity approach. As a result of the study, it was found that, despite the variety of electronic learning tools available in kindergartens in the region, there is a problem with their insufficient number, as well as the predominance of highly specialized stationary equipment with ready-made children’s content, assuming the passive role of preschoolers as viewers and consumers of information.
Editorial for the Special Issue on “Software Engineering and Data Science”, Volume II

The Special Issue “Software Engineering and Data Science, Volume II” is the natural continuation of its greatly successful predecessor, Volume I [...]
MENUMBUHKAN AKTIVITAS BELAJAR DAN HASIL BELAJAR MENGGUNAAN MODEL TGT MATERI BANGUN DATAR KELAS IV

This study aims to foster learning activities and learning outcomes of grade IV students of SDN Bendungan Kota Semarang towards the use of the Teams Games Tournament (TGT) learning model in learning mathematics flat building material. This research belongs to the type of descriptive qualitative research. The subjects taken in this study were students of class IVA SDN Damungan. Data collection techniques in the form of observations of student learning activity, student learning outcomes and questionnaires of student responses to learning using the Teams Games Tournament model. The results showed that the Teams Games Tournament model can increase students' understanding and learning activities in flat material mathematics lessons, besides that the Teams Games Tournament model is also very relevant to grow the learning outcomes of grade IVA students at SDN Bendungan Kota Semarang. From the responses of students, it was revealed that students are more interested in participating in learning using the TGT learning model because they can be more active, creative, and collaborate with their peers.&#x0D;
Three-dimensional plasmonic lithography imaging modeling based on the RCWA algorithm for computational lithography

This paper reminds the principle and characteristics of plasmonic lithography, and points out the importance of establishing a fast and high precision plasmonic lithography imaging model and developing computational lithography. According to the characteristics of plasmonic lithography, the rigorous coupled-wave analysis (RCWA) algorithm is a very suitable alternative algorithm. In this paper, a three-dimensional plasmonic lithography model based on RCWA algorithm is established for computational lithography requirements. This model improves the existing RCWA algorithm, that is, deduces the formula for calculating the light field inside the structure and proposes the integration, storage and invocation of the scattering matrix to improve the computation speed. Finally, the results are compared with commercial software for the two typical patterns. The results show that the two calculation results are very close, with the root mean square error (RMSE) less than 0.04 (V/m) 2 . In addition, the calculation speed can be increased by more than 2 times in the first calculation, and by about 8 times by integrating, storing and invoking the scattering matrix, which creates conditions for the development of plasmonic computational lithography.
Neural Network Exemplar Parallelization with Go

This paper presents a case for exemplar parallelism of neural networks using Go as parallelization framework. Further it is shown that also limited multi-core hardware systems are feasible for these parallelization tasks, as notebooks and single board computer systems. The main question was how much speedup can be generated when using concurrent Go goroutines specifically. A simple concurrent feedforward network for MNIST digit recognition with the programming language Go was created to find the answer. The first findings when using a notebook (Lenovo Yoga 2) showed a speedup of 252% when utilizing 4 goroutines. Testing a single board computer (Banana Pi M3) delivered more convincing results: 320% with 4 goroutines, and 432% with 8 goroutines.
Mixture Encoder Supporting Continuous Speech Separation for Meeting Recognition

Many real-life applications of automatic speech recognition (ASR) require processing of overlapped speech. A commonmethod involves first separating the speech into overlap-free streams and then performing ASR on the resulting signals. Recently, the inclusion of a mixture encoder in the ASR model has been proposed. This mixture encoder leverages the original overlapped speech to mitigate the effect of artifacts introduced by the speech separation. Previously, however, the method only addressed two-speaker scenarios. In this work, we extend this approach to more natural meeting contexts featuring an arbitrary number of speakers and dynamic overlaps. We evaluate the performance using different speech separators, including the powerful TF-GridNet model. Our experiments show state-of-the-art performance on the LibriCSS dataset and highlight the advantages of the mixture encoder. Furthermore, they demonstrate the strong separation of TF-GridNet which largely closes the gap between previous methods and oracle separation.
Towards Last-layer Retraining for Group Robustness with Fewer Annotations

Empirical risk minimization (ERM) of neural networks is prone to over-reliance on spurious correlations and poor generalization on minority groups. The recent deep feature reweighting (DFR) technique achieves state-of-the-art group robustness via simple last-layer retraining, but it requires held-out group and class annotations to construct a group-balanced reweighting dataset. In this work, we examine this impractical requirement and find that last-layer retraining can be surprisingly effective with no group annotations (other than for model selection) and only a handful of class annotations. We first show that last-layer retraining can greatly improve worst-group accuracy even when the reweighting dataset has only a small proportion of worst-group data. This implies a "free lunch" where holding out a subset of training data to retrain the last layer can substantially outperform ERM on the entire dataset with no additional data or annotations. To further improve group robustness, we introduce a lightweight method called selective last-layer finetuning (SELF), which constructs the reweighting dataset using misclassifications or disagreements. Our empirical and theoretical results present the first evidence that model disagreement upsamples worst-group data, enabling SELF to nearly match DFR on four well-established benchmarks across vision and language tasks with no group annotations and less than 3% of the held-out class annotations. Our code is available at https://github.com/tmlabonte/last-layer-retraining.