Reviewerly/exHarmony huggingface dataset:
A Research on Tea Traceability Consensus Mechanism Based on Blockchain Technology

By optimizing the consensus mechanism dBFT (authorized Byzantine fault-tolerant algorithm) in the tea traceability system based on blockchain technology, the voting and consensus node selection are optimized. A credit reward and punishment mechanism C-dBFT is proposed to encourage all nodes to vote, which reduces the possibility of invalid votes and abnormal sections. Points for long-term participation in consensus. The selected nodes are determined by the analytic hierarchy process and decision-making, and the main node and backup node are finally determined. Experiments show that efficient consensus makes the tea traceability system work better.
Penerapan Algoritma Backpropagation Dalam Memprediksi Jumlah Penduduk di Kecamatan Pematang Bandar Berdasarkan Nagori/Kelurahan

The number of residents in a sub-district greatly influences the the welfare of the community in the sub-district. But to determine the population each year in the Subdistrict requires a considerable amount of time and with a relatively simple method. Therefore we need a study to predict the number of population in the coming year, the algorithm used is the Backpropagation algorithm, this algorithm aims to make a prediction process for the population in Pematang Bandar Subdistrict, North Sumatra. Based on the data obtained, the population in Pematang Bandar District has increased. This study uses 5 architectural models, namely 3-2-1, 3-4-1, 3-5-1, 3-6-1, 3-9-1. Of the five architectural models used, one of the best 3-4-1 architectural models was obtained with accuracy of 92.3%, epoch 796 iterations in 10 seconds and MSE 0,000930636. It is expected that the results of this study can contribute to the District in improving all aspects of community welfare in the future.
Anomaly Detection by Recombining Gated Unsupervised Experts

Anomaly detection has been considered under several extents of prior knowledge. Unsupervised methods do not require any labelled data, whereas semi-supervised methods leverage some known anomalies. Inspired by mixture-of-experts models and the analysis of the hidden activations of neural networks, we introduce a novel data-driven anomaly detection method called ARGUE. Our method is not only applicable to unsupervised and semi-supervised environments, but also profits from prior knowledge of self-supervised settings. We designed ARGUE as a combination of dedicated expert networks, which specialise on parts of the input data. For its final decision, ARGUE fuses the distributed knowledge across the expert systems using a gated mixture-of-experts architecture. Our evaluation motivates that prior knowledge about the normal data distribution may be as valuable as known anomalies.
Strong superadditivity relations for multiqubit systems

We investigate the distributions of quantum coherence characterized by superadditivity relations in multipartite quantum systems. General superadditivity inequalities based on the $\alpha$th ($\alpha\geqslant 1$) power of $l_1$ norm of coherence are presented for multiqubit states, which include the existing ones as special cases. Our result is shown to be tighter than the existing one by a specific example.
Research on Military Aviation Air Traffic Control Safety Assessment Based on AFSA-SVM

In order to improve the safety management level of military aviation Air Traffic Control (ATC), based on Artificial Fish Swarm Algorithm (AFSA) and Support Vector Machine (SVM), a combined assessment method is proposed for military aviation ATC safety. In this method, firstly, according to the established military aviation safety evaluation index system, the concepts of set pair and connection degree in SPA theory are introduced to generate training samples and test samples. Then AFSA is adopted to optimize the hyper-parameters of SVM. Then training and testing are completed to obtain the military aviation safety evaluation model. The example analysis shows that the evaluation model established is of great practical significance for improving the safety management and decision-making level of military aviation management.
Hybrid Attention Network for Language-Based Person Search

Language-based person search retrieves images of a target person using natural language description and is a challenging fine-grained cross-modal retrieval task. A novel hybrid attention network is proposed for the task. The network includes the following three aspects: First, a cubic attention mechanism for person image, which combines cross-layer spatial attention and channel attention. It can fully excavate both important midlevel details and key high-level semantics to obtain better discriminative fine-grained feature representation of a person image. Second, a text attention network for language description, which is based on bidirectional LSTM (BiLSTM) and self-attention mechanism. It can better learn the bidirectional semantic dependency and capture the key words of sentences, so as to extract the context information and key semantic features of the language description more effectively and accurately. Third, a cross-modal attention mechanism and a joint loss function for cross-modal learning, which can pay more attention to the relevant parts between text and image features. It can better exploit both the cross-modal and intra-modal correlation and can better solve the problem of cross-modal heterogeneity. Extensive experiments have been conducted on the CUHK-PEDES dataset. Our approach obtains higher performance than state-of-the-art approaches, demonstrating the advantage of the approach we propose.
Pengelolaan Pengelolaan Konten Web Menggunakan Wordpress, Canva dan Photoshop untuk Guru-Guru Wilayah Jakarta

Web Content Management Using Wordpress, Canva and Photoshop for Teachers of the Jakarta Region&#x0D; The web is a tool that has been widely used to share information with the public. However, in managing the content, not a few parties who do not know how to do it well, including high school and vocational school teachers. Therefore, WordPress-based web content management training is applied to the teachers to assist them in running and managing the web. The training began with the provision of material on designing logos and banners using Photoshop and Canva, then practice how to register domains and training in making e-commerce with WordPress Content Management System. From the results of the training evaluation questionnaire, as many as 69% of participants were satisfied in delivering the material and 100% stated that web management training was needed to be learned and applied in schools.
Design of an ergonomic gestural interface for professional road cycling

BACKGROUND: Connected bike computers can support professional cyclists in achieving better performances but interacting with them requires taking their hands off the handlebar compromising focus and safety. OBJECTIVE: This research aims at exploring the design of an ergonomic interface based on micro-gestures that can allow cyclists to interact with a device while holding the handlebar. METHODS: Three different studies were conducted with seven professional cyclists adopting the gesture-elicitation technique. One study aimed at eliciting free micro-gestures; a second to evaluate gestures recognizable with a smart glove; the last focused on the gestures recognized through an interactive armband. RESULTS: The analysis of the micro-gestures elicited during these studies allowed producing a first set of guidelines to design gestural interfaces for drop-bars (a specific type of handlebar for road bikes). These guidelines suggest which fingers to use and how to design their movement in order to provide an ergonomic interface. It also introduces the principle of symmetry for the attribution of symbols to symmetric referents. Finally, it provides suggestions on the design of the interactive drop-bar. CONCLUSIONS: The guidelines provided in this paper can support the design of gestural interfaces for professional cyclists that can enhance performance and increase safety.
A New Citation Recommendation Strategy Based on Term Functions in Related Studies Section

Abstract Purpose Researchers frequently encounter the following problems when writing scientific articles: (1) Selecting appropriate citations to support the research idea is challenging. (2) The literature review is not conducted extensively, which leads to working on a research problem that others have well addressed. The study focuses on citation recommendation in the related studies section by applying the term function of a citation context, potentially improving the efficiency of writing a literature review. Design/methodology/approach We present nine term functions with three newly created and six identified from existing literature. Using these term functions as labels, we annotate 531 research papers in three topics to evaluate our proposed recommendation strategy. BM25 and Word2vec with VSM are implemented as the baseline models for the recommendation. Then the term function information is applied to enhance the performance. Findings The experiments show that the term function-based methods outperform the baseline methods regarding the recall, precision, and F1-score measurement, demonstrating that term functions are useful in identifying valuable citations. Research limitations The dataset is insufficient due to the complexity of annotating citation functions for paragraphs in the related studies section. More recent deep learning models should be performed to future validate the proposed approach. Practical implications The citation recommendation strategy can be helpful for valuable citation discovery, semantic scientific retrieval, and automatic literature review generation. Originality/value The proposed citation function-based citation recommendation can generate intuitive explanations of the results for users, improving the transparency, persuasiveness, and effectiveness of recommender systems.
Impact of large scale social restriction on the COVID-19 cases in East Java

Indonesia is one of the countries affected by COVID-19. The Indonesian government and local governments have issued several guidelines to limit the spread of cases that spread across people in the form of restrictions on physical activity between people. One of the guidelines is large-scale social restrictions (PSBB). PSBB is being implemented in several regions of Indonesia with high COVID-19 cases, including Jakarta, West Java and East Java. Recently, East Java became a province with the highest number of daily new cases, reaching more than 300 reported cases in one day. Some time ago, several regions in Indonesia stopped PSBB policy and took the direction of the New Normal, including East Java. This study suggests machine learning methods that are recognized based on high accuracy, containing Extreme Learning Machines, Multi-Layer Perceptron, and Auto-Regressive Neural Networks to predict the number of daily new, active, confirmed, recovered, and death cases. The MLP (10,10) model was obtained as the best model for predicting the five case variables in East Java for the next 7 days. Based on these results, it can be concluded that the cases in East Java are still increasing. According to this study, the application of the PSBB and the abolition of the PSBB directive, which was replaced by the New Normal directive, had a significant impact on the increase in cases in the East Java region. This is in line with the estimation results, which show that cases in East Java tend to increase with fluctuating new daily cases.
Success and failure of digital money and virtual money: Case of cryptocurrency-bitcoin

Digital currency is a type of currency available in digital form. Though it exhibits similar properties and value as physical currency but it can allow instant transfer of ownership and thus makes transactions smooth. It is used as a blanket term for all electronic money including both virtual currency and cryptocurrency. It can be regulated or unregulated. However virtual currency is a complicated term to understand. Though it comes under digital currency but its functions are totally different and have some issues in its regulation. It only works through designated softwares, mobiles or computer application through digital wallets. We can say that it is a subset of digital currency group. Due to lack of a centralised regulatory authority, such virtual currencies are prone to wide swings in their valuations. Despite around 77% of Indians being familiar with such currencies, the awareness about it is very low. Around 23% of consumers are unconcerned about security related issues while dealing with cryptocurrency. India has a very complicated relationship with cryptocurrency. In this paper we will discuss the success and failure levels of various forms of virtual currency, issues related to its ban in India.
Learning and Mapping Energy Functions of High-Dimensional Image Data

Author(s): Hill, Mitchell Krupiarz | Advisor(s): Zhu, Song-Chun | Abstract: Energy-based models are a powerful and flexible tool for studying emergent properties in systems with many interacting components. Energy functions of complex systems are highly non-convex because the local modes encode the rich variety of probable phenomena.This work focuses on energy models of image signals, which are treated as complex systems of interacting pixels that contribute to the emergent appearance of the whole image. By adapting classical Maximum Likelihood learning to the parametric family of ConvNet functions, one can learn energy-based models capable of realistic image synthesis. The observed behaviors of ML learning with ConvNet potentials are surprising and not well understood despite widespread use of the potential in recent studies. This work rigorously diagnoses the learning process to correct systematic problems with steady-state distributions of learned potentials while revealing an unexpected non-convergent outcome of learning.The true value of the energy-based model lies in the structure of the energy landscape. The energy-based model encodes explicit quantitative relations between local modes in landscape energy barriers. Concepts in the training data form macroscopic energy basins with many shallow local modes. Powered by a new Markov Chain Monte Carlo algorithm that efficiently detects macroscopic energy structures, energy landscape mapping experiments are conducted to discover meaningful image concepts. The mapping framework is a powerful tool for unsupervised clustering based on geodesic distances in a learned potential.
Tomorrow isn’t always a day away

This paper explores non-utterance time readings of tomorrow, which, though unexpected under the standard pure indexical view of tomorrow, are attested in American English: e.g., “Last week, UPS said that the package would be delivered tomorrow.” This example has two readings: an utterance time reading, which can be felicitously followed by “I hope it arrives on time!”, and a non-utterance time reading, which can be followed by “But it never showed up!” I present experimental evidence that (1) non-utterance time readings of tomorrow are acceptable for many American English speakers and (2) not due to Free Indirect Discourse or indexical shift. Instead, I propose an analysis of tomorrow as anaphoric to a salient perspective.
Learning to be a teacher without being in the classroom: COVID-19 as a threat to the professional development of future teachers/ Aprender a ser docente sin estar en las aulas: La COVID-19 como amenaza al desarrollo profesional del futuro profesorado

Teachers in initial training who attend the Practicum find, in this period, a meaning for the teaching profession, awakening a reflective and critical attitude about what and how they do things, and therefore, shaping their teaching identity. However, in recent months, the training situation of future teachers has taken a turn due to the health crisis caused by the COVID-19 pandemic. The study carried out focused on inquiring, through a narrative research, how university students have perceived the period of practices they carried out through virtual teaching, as a consequence of the current health crisis. Among other results, the evidence showed that students developed insecurity feelings, melancholy and uncertainty in light of the new learning processes. On the other hand, by losing on-site classroom attendance and with it the essence of the practices, the training potential of the Practicum and the teaching professional identity of the students have been affected during these months, consequences that could affect their future professional practices.
Delving Deeper into the Decoder for Video Captioning.

Video captioning is an advanced multi-modal task which aims to describe a video clip using a natural language sentence. The encoder-decoder framework is the most popular paradigm for this task in recent years. However, there exist some problems in the decoder of a video captioning model. We make a thorough investigation into the decoder and adopt three techniques to improve the performance of the model. First of all, a combination of variational dropout and layer normalization is embedded into a recurrent unit to alleviate the problem of overfitting. Secondly, a new online method is proposed to evaluate the performance of a model on a validation set so as to select the best checkpoint for testing. Finally, a new training strategy called professional learning is proposed which uses the strengths of a captioning model and bypasses its weaknesses. It is demonstrated in the experiments on Microsoft Research Video Description Corpus (MSVD) and MSR-Video to Text (MSR-VTT) datasets that our model has achieved the best results evaluated by BLEU, CIDEr, METEOR and ROUGE-L metrics with significant gains of up to 18% on MSVD and 3.5% on MSR-VTT compared with the previous state-of-the-art models.
A Fuzzy-Based Multi-Modal Architectural Model for Electioneering in Nigeria

The 2015 election has been adjured to be the best in the history of elections in Nigeria, thanks to the deployment of information technology by the Independent National Electoral Commission (INEC). In reality, the election was not as flawless as many people believe. There were lots of problems associated with it, particularly in the areas of verification and authentication of eligible voters. This work examines these problems and proposed a fuzzy-based multi-modal architectural model for future elections in contrast to the unimodal model currently in use. The architecture is based on the extraction of the features of fingerprint and iris of the prospective voters as opposed to only fingerprint currently in use. Drawing from the strength of fingerprint and Iris features discussed in the work, a robust architecture supported by the fuzzy-based mathematical model was developed for future elections. Further discussed were the advantages of the proposed architecture over the current INEC method of voter verification and authentication.
Evaluating Multimodal Representations on Visual Semantic Textual Similarity

The combination of visual and textual representations has produced excellent results in tasks such as image captioning and visual question answering, but the inference capabilities of multimodal representations are largely untested. In the case of textual representations, inference tasks such as Textual Entailment and Semantic Textual Similarity have been often used to benchmark the quality of textual representations. The long term goal of our research is to devise multimodal representation techniques that improve current inference capabilities. We thus present a novel task, Visual Semantic Textual Similarity (vSTS), where such inference ability can be tested directly. Given two items comprised each by an image and its accompanying caption, vSTS systems need to assess the degree to which the captions in context are semantically equivalent to each other. Our experiments using simple multimodal representations show that the addition of image representations produces better inference, compared to text-only representations. The improvement is observed both when directly computing the similarity between the representations of the two items, and when learning a siamese network based on vSTS training data. Our work shows, for the first time, the successful contribution of visual information to textual inference, with ample room for benchmarking more complex multimodal representation options.
Şifrelenmiş Verileri Rast Gele Piksel Yaklaşımı ile Bir Görüntüye Gömme

With the recent developments in information and communication technologies, data is transferred to digital environments. However, it has become very important to protect the information in digital environments. This situation raises the need for information security. Cryptography and steganography are among the most effective areas in ensuring information security. This study was carried out in order to contribute to information security by using cryptography at the point of making data meaningless by encrypting, and steganography at the point of hiding meaningless data. In this study, random pixel layout approach is used to hide the encrypted data into a 24-bit image. The proposed technique was examined on images with different file types and resolutions. The Structural Similarity Index Measure (SSIM), Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) parameters were revealed to measure the effectiveness of the proposed approach. It has been observed from the application that this method increases the security level of embedding data process.
A Text-Based Deep Reinforcement Learning Framework for Interactive Recommendation.

Due to its nature of learning from dynamic interactions and planning for long-run performance, reinforcement learning (RL) recently has received much attention in interactive recommender systems (IRSs). IRSs usually face the large discrete action space problem, which makes most of the existing RL-based recommendation methods inefficient. Moreover, data sparsity is another challenging problem that most IRSs are confronted with. While the textual information like reviews and descriptions is less sensitive to sparsity, existing RL-based recommendation methods either neglect or are not suitable for incorporating textual information. To address these two problems, in this paper, we propose a Text-based Deep Deterministic Policy Gradient framework (TDDPG-Rec) for IRSs. Specifically, we leverage textual information to map items and users into a feature space, which greatly alleviates the sparsity problem. Moreover, we design an effective method to construct an action candidate set. By the policy vector dynamically learned from TDDPG-Rec that expresses the user's preference, we can select actions from the candidate set effectively. Through experiments on three public datasets, we demonstrate that TDDPG-Rec achieves state-of-the-art performance over several baselines in a time-efficient manner.