Reviewerly/exHarmony huggingface dataset:
Intelligent Integration Method of Big Data for Time Sharing Regulation of Elastic Load

At present, the intelligent data integration method requires a large number of professionals to integrate the time-sharing control big data of electric elastic load. After the integration, there is a large amount of redundant data and the data integration efficiency is low. Therefore, an intelligent integration method of time-sharing regulation big data of electric elastic load is proposed. The data parallel state is calculated, the mathematical description of data parallel state is analyzed, and the change of electric power data in the time-sharing regulation and control of power elastic load is analyzed, and the mathematical description result is modified to further improve the data state analysis result. According to the obtained data state description result, the data state is decomposed to reduce the difficulty of data intelligent integration; data intelligence is designed Integration process, the data in the data intelligent integration of secondary planning, reduce the redundant data in the data, complete the data intelligent integration. The experimental results show that the intelligent data integration method in this study integrates the time-sharing control big data of electric elastic load, which makes the results of data integration more accurate, improves the rate of deleting redundant data, and reduces the redundancy of the integrated data set.
Compensatory Adaptive Neural Fuzzy Inference System

The traditional approach to fuzzy design is based on knowledge acquired by expert operators formulated into rules. However, operators may not be able to translate their knowledge and experience into a fuzzy logic controller. In addition, most adaptive fuzzy controllers present difficulties in determining appropriate fuzzy rules and appropriate membership functions. This chapter presents adaptive neural-fuzzy controller equipped with compensatory fuzzy control in order to adjust membership functions, and as well to optimize the adaptive reasoning by using a compensatory learning algorithm. An analysis of stability and transparency based on a passivity framework is carried out. The resulting controllers are implemented on a two degree of freedom robotic system. The simulation results obtained show a fairly high accuracy in terms of position and velocity tracking, what highlights the effectiveness of the proposed controllers.
«The Right Thing» and «The Wrong Thing» as a Result of Discerning Signals

The article is dedicated to studying the process of information perception by an individual. A hypothesis is suggested that the environment that surrounds an individual is full of different signals, which have na­tural, social or technogenic origin. Separate signals can turn into information, if the recipient is able of discerning these signals and perceives them on the physical, intellectual, emotional and axiological levels of information perception. Whereas the complex of signals which are indiscernible for an individual are external noise. The authors state that interpretation of a signal by individuals begins on the physical level of perception which is necessary to synchronize their activity with that of the source of the signal. After that the individual has to move to the axiological level of information perception in order to coordinate the notions and meanings with the source of the signal. Furthermore, the authors substantiate the possibility of revealing two components in the structure of the discerned signal: «The Right Thing» and «The Wrong Thing» as an inherent beginning of any process of perceiving the outside world by an individual. The suggested hypothesis is illustrated in the article by the example of two kinds of information message: a painting by A. Deyneka and a poem by A. Blok which are studied in terms of the proposed hypothesis about the existence of levels of information perception and the possibility of discerning two basic elements in them: «The Right Thing» and «The Wrong Thing». The results of the research can be applied to improve the process of communicating and perceiving information.
SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN TINGKAT MINAT MASYARAKAT DALAM MEMILIH TANAMAN HIAS MENGGUNAKAN METODE SAW

Ornamental plants are plants that have aesthetic and aesthetic value, often used to decorate yards, gardens or rooms in homes, office buildings, hotels, restaurants. Like fashion, trends in ornamental plants come and go and people have tastes that tend to change. Ornamental plants such as Aglonema, Anthurium, Calathea, Monstera Adansoni, Sansevieria are currently the prima donna and in demand by the public. To assist in determining and knowing which ornamental plants are most in demand by the public, it is necessary to build a decision support system using the SAW method. The SAW method is a weighted addition method that has the ability to solve problems from several criteria, so that it will produce alternative ornamental plants based on their level of interest (the most desirable) more quickly and precisely. There are several criteria used in the decision support system to determine the level of public interest in choosing ornamental plants using the SAW method, namely the shape / type of leaf, leaf color, price and care.
파자구(把字句)와 (역-)피동태에 관한 소고 ― 문법태의 각도에서 바라본 파자구(把字句)

In this paper, we investigated Ba-construction, anti-passive voice and passive voice. Although Yeh & Pan(2012) were meaningful in that they provided new perspectives on Ba-construction, there were problems to reconsider.Yeh & Pan regarded Ba-construction as a kind of anti-passive construction and said it was de-transitivized, but Ba-NP was not demoted, which was originally placed before verb and that construction is still a high-transitive construction.They also said that there are some cases where Ba-NP are indefinite, but in this case Ba-NP still has definite meanings with the help of other factors, such as main verb and context. In addition, in some cases of ‘BageNP’ the ‘ge’ played a role in highlighting quality or attribute rather than the function of indicating definite and indefiniteThe decisive reason for not seeing Ba construction as an anti-passive construction lies in the fact that the demoted object of an anti-passive construction can be deleted, while the Ba-NP must never be deleted in any case.Meanwhile, Ba-construction and Bei-construction had many things in common and have been discussed together by many scholars. However there are also many differences between two constructions, because the operating mechanism itself is different.Therefore, we thought Ba-construction is still a kind of active voice construction, some non-disposal examples are directly related to causative voice(zhishi), and anti-passive and passive voice are only indirectly related with this construction. In particular, anti-passive voice can be said that it is almost opposite from Ba-construction.
A novel method to calculate the electric field using solid angles

A new method to calculate the electric field inside a spherical shell with surface charge in terms of solid angle is presented. The integral can be readily carried out without invoking special functions typically used for this classical problem. For a flat surface of uniform charge density, the electric field normal to the surface is shown to be proportional to the solid angle subtended by the surface only.
Pseudo Label-Guided Multi Task Learning for Scene Understanding

Multi-task learning (MTL) for scene understanding has been actively studied by exploiting correlation of multiple tasks. This work focuses on improving the performance of the MTL network that infers depth and semantic segmentation maps from a single image. Specifically, we propose a novel MTL architecture, called Pseudo-MTL, that introduces pseudo labels for joint learning of monocular depth estimation and semantic segmentation tasks. The pseudo ground truth depth maps, generated from pretrained stereo matching methods, are leveraged to supervise the monocular depth estimation. More importantly, the pseudo depth labels serve to impose a cross-view consistency on the estimated monocular depth and segmentation maps of two views. This enables for mitigating the mismatch problem incurred by inconsistent prediction results across two views. A thorough ablation study validates that the cross-view consistency leads to a substantial performance gain by ensuring inference-view invariance for the two tasks.
Form of Hate Speech Comments on Najwa Shihab Youtube Channels in The General Election Campaign of President and Vice President of The Republic of Indonesia 2019

Emerging and spreading hate speech online was a growing phenomenon in social media on youtube. Najwa Shihab was a youtube channel that contains shows and comments on political issues in Indonesia, one of them was the 2019 presidential election campaign. The form of hate speech in comments in the Najwa Shihab Channels was forming grammatical construction and construction of the meaning of one hate speech with other utterances. The purpose of this study was to analyze the illocutionary speech acts on hate speech and to analyze hate speech based on the rule of law in Indonesia. The analysis resulted in hate speech on the Najwa Shihab youtube channel audience comments in the General Election campaign in the discourse of the President and Vice President of the Republic of Indonesia in 2019. The approach that used in this study was a theoretical approach, namely a forensic linguistic approach by utilizing pragmatics for data analysis, and a methodological approach, namely a qualitative descriptive approach. The data on hate speech that was analyzed was a fragment of the audience's comments on two programs, namely Mata Najwa and Catatan Najwa. The results showed that there were nine forms of hate speech. Two forms of hate speech were considered to be the style of speech that was widely used by the public in delivering comments on social media. The forms of the speech were "Form of Hate Speech, Assertive Speech - Insult" and "Form of Hate Speech, Assertive Speech - Defamation". The existence of this form of speech can be a reference for the community to process opinions first before conveying it on social media. It's legally can be used as an offense in cases of hate speech.
Anytime Sampling for Autoregressive Models via Ordered Autoencoding

Autoregressive models are widely used for tasks such as image and audio generation. The sampling process of these models, however, does not allow interruptions and cannot adapt to real-time computational resources. This challenge impedes the deployment of powerful autoregressive models, which involve a slow sampling process that is sequential in nature and typically scales linearly with respect to the data dimension. To address this difficulty, we propose a new family of autoregressive models that enables anytime sampling. Inspired by Principal Component Analysis, we learn a structured representation space where dimensions are ordered based on their importance with respect to reconstruction. Using an autoregressive model in this latent space, we trade off sample quality for computational efficiency by truncating the generation process before decoding into the original data space. Experimentally, we demonstrate in several image and audio generation tasks that sample quality degrades gracefully as we reduce the computational budget for sampling. The approach suffers almost no loss in sample quality (measured by FID) using only 60\% to 80\% of all latent dimensions for image data. Code is available at https://github.com/Newbeeer/Anytime-Auto-Regressive-Model .
Counterfactual Self-Training

Unlike traditional supervised learning, in many settings only partial feedback is available. We may only observe outcomes for the chosen actions, but not the counterfactual outcomes associated with other alternatives. Such settings encompass a wide variety of applications including pricing, online marketing and precision medicine. A key challenge is that observational data are influenced by historical policies deployed in the system, yielding a biased data distribution. We approach this task as a domain adaptation problem and propose a self-training algorithm which imputes outcomes for the unseen actions in the observational data to simulate a randomized trial. We offer a theoretical motivation for this approach by providing an upper bound on the generalization error defined on a randomized trial under the self-training objective. We empirically demonstrate the effectiveness of the proposed algorithms on both synthetic and real datasets.
Enhancing Visual Representations for Efficient Object Recognition during Online Distillation

We propose ENVISE, an online distillation framework that ENhances VISual representations for Efficient object recognition. We are motivated by the observation that in many real-world scenarios, the probability of occurrence of all classes is not the same and only a subset of classes occur frequently. Exploiting this fact, we aim to reduce the computations of our framework by employing a binary student network (BSN) to learn the frequently occurring classes using the pseudo-labels generated by the teacher network (TN) on an unlabeled image stream. To maintain overall accuracy, the BSN must also accurately determine when a rare (or unknown) class is present in the image stream so that the TN can be used in such cases. To achieve this, we propose an attention triplet loss which ensures that the BSN emphasizes the same semantically meaningful regions of the image as the TN. When the prior class probabilities in the image stream vary, we demonstrate that the BSN adapts to the TN faster than the real-valued student network. We also introduce Gain in Efficiency (GiE), a new metric which estimates the relative reduction in FLOPS based on the number of times the BSN and TN are used to process the image stream. We benchmark CIFAR-100 and tiny-imagenet datasets by creating meaningful inlier (frequent) and outlier (rare) class pairs that mimic real-world scenarios. We show that ENVISE outperforms state-of-the-art (SOTA) outlier detection methods in terms of GiE, and also achieves greater separation between inlier and outlier classes in the feature space.
Quantum Software EngineeringSupremacy in Intelligent Robotics

A new approach for implementing quantum massive parallel computations is presented, using methods of circuit implementation of quantum algorithmic gates. Methods for designing fast quantum operators such as superposition, entanglement, andinterference are considered. The presented methodsallow you to reduce the number of actions that must be performed. The implementation is presented as a support tool for SW&amp;HW supercomputer accelerator for modeling quantum algorithms. In particular, a newquantum-genetic and quantum-fuzzy inference algorithm for intelligent robotic control has been implemented. Also, a new method for performing Grover's inference without operations with the productis presented.
Revisiting the details when evaluating a visual tracker

Visual tracking algorithms are naturally adopted in various applications, there have been several benchmarks and many tracking algorithms, more expected to appear in the future. In this report, I focus on single object tracking and revisit the details of tracker evaluation based on widely used OTB\cite{otb} benchmark by introducing a simpler, accurate, and extensible method for tracker evaluation and comparison. Experimental results suggest that there may not be an absolute winner among tracking algorithms. We have to perform detailed analysis to select suitable trackers for use cases.
Impact of Agribusiness Training on Youth Empowerment: A Case Study of Fadama Graduate Unemployed Youth and Women Support (GUYS) Programme in Nigeria

Agripreneurship is increasingly being recognized as an important and valuable strategy to reduce the high dependency of young people on white-collar jobs as well as increase employment opportunities in the non-formal sector. Thus, it has become one of the key African governments’ investments in the creation of sustainable employment as well as improvement of livelihoods of young people. Based on this, development stakeholders in many African countries have come together in recent times to support agripreneurship by organizing agribusiness training programmes which specifically target this category of the population. Despite the numerous training interventions, there is a dearth of empirical evidence on what has worked and what has not. Using the case of Fadama Graduate Unemployed Youth and Women Support (GUYS) programme, this study used the Propensity Score Matching (PSM) method to investigate the impact of agribusiness training on youth empowerment in Nigeria. A total of 977 respondents comprising of 455 participants and 522 non-participants were sampled across three states. PSM model results showed that after controlling for all confounding factors, participation in training in the Programme had about 11 percent increase in youth empowerment index score. This implied a positive change in the economic status and livelihoods of the youths who participated in the agribusiness training of the Programme. Thus, the study suggests that programmes such as the Fadama GUYS should be encouraged and out-scaled elsewhere in Africa as they can inspire youths to engage in agribusiness and thereby contribute to reduction of youth unemployment as well as enhancement of youth empowerment. Keywords: Agribusiness, Youth empowerment, Youth unemployment, Agribusiness training
Classical-Quantum Multiple Access Wiretap Channel with Common Message: One-Shot Rate Region

The following topics are dealt with: learning (artificial intelligence); graph theory; text analysis; neural nets; natural language processing; data mining; telecommunication security; statistical analysis; feature extraction; and social networking (online).
Fast Entropy Coding for ALICE Run 3

In LHC Run 3, the upgraded ALICE detector will record Pb-Pb collisions at a rate of 50 kHz usingcontinuous readout. The resulting stream of raw data at 3.5 TB/s has to be processed with a setof lossy and lossless compression and data reduction techniques to a storage data rate of 90 GB/swhile preserving relevant data for physics analysis. This contribution presents a custom losslessdata compression scheme based on entropy coding as the final component in the data reductionchain which has to compress the data rate from 300 GB/s to 90 GB/s. A flexible, multi-processarchitecture for the data compression scheme is proposed that seamlessly interfaces with the datareduction algorithms of earlier stages and allows to use parallel processing in order to keep therequired firm real-time guarantees of the system. The data processed inside the compressionprocess have a structure that allows the use of an rANS entropy coder with more resource efficientstatic distribution tables. Extensions to the rANS entropy coder are introduced to efficientlywork with these static distribution tables and large but sparse source alphabets consisting of upto 25 Bit per symbol. Preliminary performance results show compliance with the firm real-timerequirements while offering close-to-optimal data compression.
Penentuan Cluster Optimum pada Tingkat Pengangguran dan Tingkat Kemiskinan di Jawa Timur Menggunakan Algoritma Fuzzy C-Means

There are many methods used in resolving data clustering. One of them is the Fuzzy C-Means (FCM) method, which is a reliable method to solve clustering problems in the East Java region. This study aims to determine the optimum cluster in the East Java region which can help the government to identify problems and assist policymaking in regencies/cities in East Java province. The research process uses data from the central statistical agency, namely the unemployment rate and poverty rate from 2010 to 2015. In this study, the Davies Bouldin Index (DBI) is used as a cluster validation test for determining the optimum cluster. Unemployment rate and poverty rate data were analyzed using RStudio. From the calculation of the FCM method and also the determination of the optimum cluster results obtained in 2 clusters with a DBI value of 1.2759, 3 clusters with a DBI value of 0.9937, 4 clusters with a DBI value of 0.8737. The optimum cluster is in 4 clusters with a minimum DBI value.
Recognition Method of Single Hand over Shoulder Shooting based on Multi-Dimensional Visual Images

When the current action recognition methods recognize one hand over the shoulder shooting action, it will cause noise and holes, which will affect the recognition of one hand over the shoulder shot action, resulting in low accuracy rate of single hand over shoulder shot action recognition. Therefore, this paper proposes a recognition method of one hand over shoulder shot action based on multi-dimensional visual images. The image acquisition model of one hand over the shoulder shot is established, and the multi-dimensional visual image is used to mark the single hand over the shoulder shooting action; the image segmentation and feature extraction are preprocessed according to the marking results of the single hand shoulder shot action; the four-dimensional vector of the single hand over shoulder shot image is established to calculate the movement process of the one hand over the shoulder shot action and identify the single hand over shoulder shot action. The experimental results show that under the background of the same one handed shoulder shot and arm motion track, the arm motion track can be identified and the recognition accuracy rate can be detected. The identification method of single hand shoulder shot action can accurately identify the single hand shoulder shot action and the arm movement track, and the recognition accuracy rate is high.
Exploring Influence of Virtual Reality-Based English Learning Activities Towards Secondary Students’ English Writing Achievement

The researcher conducted an experimental research of using Virtual Reality as instructional media in form of Virtual Reality-based activities especially in learning English in form of writing skill. This research had two purposes, namely (1) to determine whether there is an effect of Virtual Reality-based English learning activities towards students’ English learning achievement in a public secondary school in North Bali, (2) to find out how VR-based English learning activities affects students’ English learning achievement in form of writing achievement. The research design used in this study was explanatory sequential explanatory mixed methods design. The population was first grade students’ of a public secondary school in North Bali. The result of qualitative data analysis showed that there is an effect of Virtual Reality-based English learning activities towards 10th grade students’ English learning achievement in public secondary school in North Bali. The Effect Size of this quantitative research is in range on low effect size. The result of qualitative data showed that there were strengths and weaknesses of using VR-based English learning activities that affects the result of quantitative data of the students’ English learning achievement in a public secondary school in North Bali in form of writing skill.&#x0D;